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Journal articles on the topic 'Network-based IDPS'
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Shalvi, Dave, Trivedi Bhushan, and Mahadevia Jimit. "EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WIRED AND WIRELESS ENVIRONMENT." International Journal of Network Security & Its Applications (IJNSA) 5, no. 2 (2013): 103–15. https://doi.org/10.5281/zenodo.3980296.
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Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks. In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
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Dubey, Bhanu Prakash. "A Machine Learning-based Approach for Intrusion Detection and Prevention in Computer Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2020): 2076–86. http://dx.doi.org/10.17762/turcomat.v11i3.13605.
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The potential of cyberattacks and network penetration has increased due to modern enterprises' increasing reliance on computer networks. Such attacks are detected and prevented by intrusion detection and prevention systems (IDPS), although conventional rule-based solutions have difficulties identifying unidentified attacks. Due to its capacity to learn from data and spot patterns of assault that conventional methods could miss, machine learning (ML) techniques have been gaining prominence in IDPS. This article provides a thorough analysis of the several ML methods utilized in IDPS, including supervised, unsupervised, and hybrid techniques. Also, a hybrid ML-based IDPS that combines the advantages of several methodologies for better performance is proposed. Furthermore, covered are the difficulties with ML-based IDPS and potential solutions. It is demonstrated how ML-based IDPS may be applied in real-world situations, emphasizing the advantages of applying ML to intrusion detection and prevention. In conclusion, this study offers insights into the most recent methods for ML-based IDPS and their potential to enhance network security.
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Hadi, Hassan Jalil, Mubashir Adnan, Yue Cao, et al. "iKern: Advanced Intrusion Detection and Prevention at the Kernel Level Using eBPF." Technologies 12, no. 8 (2024): 122. http://dx.doi.org/10.3390/technologies12080122.
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The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods.
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Kulkarni, Prakash, Vitor B. P. Leite, Susmita Roy, et al. "Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma." Biophysics Reviews 3, no. 1 (2022): 011306. http://dx.doi.org/10.1063/5.0080512.
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Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and “promiscuous” interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.
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Et. al., K. NandhaKumar,. "A Hybrid Adaptive Development Algorithm and Machine Learning Based Method for Intrusion Detection and Prevention System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1226–36. http://dx.doi.org/10.17762/turcomat.v12i5.1789.
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Network Intrusion detection and prevention Systems (NIDPS) are employed in monitoring a network which safeguards user integrity, privacy thereby ensuring the data security and availability in a network. Such systems not only monitor the suspicious activities in a network but also used as control systems to eliminate the malicious users from the network. In this paper, a Hybrid Adaptive Development Algorithm and Machine Learning Algorithm (ADA-MLA) method is proposed to identify the malicious activities and eliminating them from the network. The deployment of honeypot-based intrusion is improved adaptive development algorithm. Machine learning algorithm has been employed in the Hybrid IDPS for learning the network data patterns which also identifies the maximum probable attacks in the network. The signatures for the DARPA 99 data set have been updated during the implementation of intrusion prevention system on a real-time basis. The hybrid method works on (i) classifying the attacks based on protocols and (ii) classifying the attacks on pre-determined threshold values. Hence, both known and unknown attacks can be easily captured in the proposed hybrid IDPS method which thereby achieves higher attack detection and prevention accuracy while compared to the conventional attack detection and prevention methodologies.
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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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Afzal, Shehroz, and Jamil Asim. "Systematic Literature Review over IDPS, Classification and Application in its Different Areas." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 3, no. 2 (2021): 189–223. http://dx.doi.org/10.52700/scir.v3i2.58.
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Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). Network security is vital for any organization connected to the Internet. Rock solid network security is a major challenge that can be overcome by strengthening the network against threats such as hackers, malware, botnets, data thieves, etc. Firewalls, antivirus, and intrusion detection systems are used to protect the network. The firewall can control network traffic, but reliance on this type of security alone is not enough. Attackers use open ports such as port 80 of the web server (http) and port 110 of the POP server to infiltrate networks. The Intrusion Detection System (IDS) minimizes security breaches and improves network security by scanning network packets to filter out malicious packets. Real-time detection with prevention using Intrusion Detection and Prevention Systems (IDPS) has elevated network security to an advanced level by strengthening the network against malicious activities. In this Survey paper focuses on Classifying various kinds of IDS with the major types of attacks based on intrusion methods. Presenting a classification of network anomaly IDS evaluation metrics and discussion on the importance of the feature selection. Evaluation of available IDS datasets discussing the challenges of evasion techniques.
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Ibnu, Hunais, Yamin Muh., and Subardin. "PENERAPAN KEAMANAN JARINGAN MENGGUNAKAN METODE HOST BASED IDPS WLAN DAN LAN BERBASIS WEB DAN SMS GATEWAY." semanTIK Vol 6 No 1 Jan-Jun 2020 (June 20, 2020): 131–38. https://doi.org/10.5281/zenodo.3892958.
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Perkembangan teknologi keamanan jaringan saat ini semakin cepat, khususnya teknologi keamanan jaringan yang menjadi salah satu teknologi yang harus diperhatikan ketika suatu perangkat atau teknologi terkoneksi dengan jaringan internet. Maraknya kasus serangan pada jaringan komputer terjadi karena tanpa disadari bahwa pihak komputer yang diserang tidak mengetahui bahwa telah terjadi serangan didalam sistemnya. Salah satu dari teknologi perkembangan jaringan komputer adalah <em>Hotspot</em> atau <em>Wireless Local Area Network</em> (WLAN) dan juga <em>Local Area Network</em> (LAN). Metode <em>Host Based Intruction Detection and Prevention System</em> (IDPS) menggunakan snort, barnyard2, dan BASE atau IDPS digunakan sebagai aplikasi untuk memantau aktifitas lalu lintas jaringan, mendeteksi dan mencegah serangan dengan cara memblokir terhadap <em>Internet Protocol</em> (IP) penyerang pada port ICMP, FTP, SSH, TELNET dengan menggunakan berbagai macam <em>tools</em> penyerang seperti <em>Angry IP Scanner, Filezilla, Putty.</em> Hasil dari penelitian ini yaitu serangan terhadap port-port tersebut berhasil diblok, dan lalu lintas data yang dianggap berbahaya akan diproses sebagai notifikasi yang dikirimkan ke administrator melalui SMS (<em>Short Message Service</em>) dan Website
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G., Florance, and R. J. Anandhi. "Empowering SDN with DDoS attack detection: leveraging hybrid machine learning based IDPS controller for robust security." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 2479. https://doi.org/10.11591/ijai.v14.i3.pp2479-2489.
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<p>Software-defined network (SDN) is an innovative networking framework where a centralized controller manages networking administration and sorts out network traffic issues. It becomes difficult for the controller to identify the malicious user who is sending a large number of spoofed packets, such as in a distributed denial of service (DDoS) attack. To prevent DDoS attacks from damaging legitimate users, it is important to take steps to prevent them. The issue of preventing DDoS attacks in SDN remains unresolved despite many algorithms proposed. Methods presented in this paper employ bandwidth threshold estimation, which triggers the intrusion detection and prevention system (IDPS) controller if the threshold is exceeded. Whenever the threshold is exceeded due to network congestion, transferred packets are filtered at the server level by identifying the utilization of bandwidth in OpenDaylight (ODL) and POX. K-nearest neighbor (K-NN) and support vector machine (SVM) are used by the IDPS controller to detect and thwart DDoS attacks. Using Mininet, two SDN centralized controllers are simulated to improve performance significantly. Based on SVM in the ODL controller, this work has provided mitigation techniques for preventing DDoS attacks with an accuracy of 96.75% compared to previously published accuracy.</p>
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Bálint, Krisztián. "Possible Cisco-based Fire Protection Solutions in Education Institutions." Műszaki Tudományos Közlemények 11, no. 1 (2019): 31–34. http://dx.doi.org/10.33894/mtk-2019.11.04.
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Abstract Solutions based on Cisco firewall protection provide numerous possibilities for more efficient protection of the abundant quantity of data that is necessary for the operation of an educational institution. Firstly, data phishing can be complicated by the constitution of a virtual network. The IDPS-based access system enables the management center to identify a potential threat in a timely manner. Furthermore, the Cisco-type firewall of a new generation is able to verify the encrypted data in a way that avoids decoding and listening the communication itself. The AAA framework is also an imperative, as in case of a network, control of access is of the utmost importance.
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Subasree, S., Michael Jeniston S. Christ, M. Harish, V. Jeevaranjan, and S. Manikandan. "Enhanced Security through Intrusion Detection and Prevention System." Recent Trends in Cyber Criminology Research 1, no. 2 (2025): 1–5. https://doi.org/10.5281/zenodo.15582270.
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<em>This paper presents the design, implementation, and evaluation of a rule-based Intrusion Detection and Prevention System (IDPS) for network security. The proposed system employs signature-based detection, anomaly-based heuristics, and active prevention techniques to detect and mitigate various types of network intrusions with high efficiency. The system was developed to address the growing challenges in network security by providing a lightweight and effective approach that does not rely on machine learning algorithms. Our implementation utilizes Scapy for packet manipulation, custom rule engines, and stateful inspection to analyse network traffic patterns. Experimental results demonstrate that our system achieves over 95% detection accuracy while maintaining low false positive rates. The implementation proves practical viability for real-world deployment in diverse network environments, offering enhanced protection against common attack vectors including DoS, probe attempts, and unauthorized access.</em>
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Duff, Eugene, Fernando Zelaya, Fidel Alfaro Almagro, et al. "Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study." PLOS ONE 17, no. 9 (2022): e0273704. http://dx.doi.org/10.1371/journal.pone.0273704.
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Introduction Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King’s College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. Conclusion These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study.
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Fang, Chun, Yoshitaka Moriwaki, Aikui Tian, Caihong Li, and Kentaro Shimizu. "Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method." Journal of Bioinformatics and Computational Biology 17, no. 01 (2019): 1950004. http://dx.doi.org/10.1142/s0219720019500045.
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Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoRFs is essential for both functional studies of IDPs and drug design. This study adopts the cutting-edge machine learning method of artificial intelligence to develop a powerful model for improving MoRFs prediction. We proposed a method, named as en_DCNNMoRF (ensemble deep convolutional neural network-based MoRF predictor). It combines the outcomes of two independent deep convolutional neural network (DCNN) classifiers that take advantage of different features. The first, DCNNMoRF1, employs position-specific scoring matrix (PSSM) and 22 types of amino acid-related factors to describe protein sequences. The second, DCNNMoRF2, employs PSSM and 13 types of amino acid indexes to describe protein sequences. For both single classifiers, DCNN with a novel two-dimensional attention mechanism was adopted, and an average strategy was added to further process the output probabilities of each DCNN model. Finally, en_DCNNMoRF combined the two models by averaging their final scores. When compared with other well-known tools applied to the same datasets, the accuracy of the novel proposed method was comparable with that of state-of-the-art methods. The related web server can be accessed freely via http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/en_MoRFs.php .
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Fang, Chun, Yoshitaka Moriwaki, Caihong Li, and Kentaro Shimizu. "MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy." Journal of Bioinformatics and Computational Biology 17, no. 06 (2019): 1940015. http://dx.doi.org/10.1142/s0219720019400158.
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Molecular recognition features (MoRFs) usually act as “hub” sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/MoRFPred_en.php .
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Roopesh, Ms. "CYBERSECURITY SOLUTIONS AND PRACTICES: FIREWALLS, INTRUSION DETECTION/PREVENTION, ENCRYPTION, MULTI-FACTOR AUTHENTICATION." ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION & SUSTAINABILITY 4, no. 3 (2024): 37–52. http://dx.doi.org/10.69593/ajbais.v4i3.90.
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In today's digitally interconnected world, cybersecurity is paramount for protecting sensitive information from sophisticated threats. This literature review examines four key cybersecurity solutions—firewalls, intrusion detection and prevention systems (IDPS), encryption, and multi-factor authentication (MFA)—highlighting their roles, advancements, and challenges based on 105 articles. Firewalls (n=35), including packet-filtering, stateful inspection, proxy, and next-generation firewalls (NGFWs), act as barriers controlling network traffic. NGFWs integrate deep packet inspection and application awareness, enhancing security despite complex maintenance issues. IDPS technologies (n=30) have evolved from anomaly detection to AI-integrated systems, improving threat detection while facing false-positive rates and zero-day exploit challenges. Encryption (n=25) ensures data confidentiality, progressing from basic ciphers to algorithms like AES and post-quantum cryptography, though it grapples with computational and key management complexities. MFA (n=15) enhances security through multiple verification factors, evolving from passwords to biometrics and behavioral analytics, yet faces user inconvenience and potential bypass methods. A comparative analysis reveals that firewalls and IDPS effectively prevent and detect threats but require meticulous management; encryption demands efficient key management; and MFA strengthens authentication but may encounter user resistance. Integrating these solutions within a layered security framework provides comprehensive protection, leveraging their strengths for a resilient security posture. Case studies affirm that multi-layered security approaches reduce breaches, underscoring the effectiveness of integrated cybersecurity practices. Continuous innovation, user education, and adaptive management are vital for addressing dynamic cyber threats, reinforcing the need for a robust, multi-faceted cybersecurity strategy.
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Farea, Ali H., and Kerem Küçük. "Machine Learning-based Intrusion Detection Technique for IoT: Simulation with Cooja." International Journal of Computer Network and Information Security 16, no. 1 (2024): 1–23. http://dx.doi.org/10.5815/ijcnis.2024.01.01.
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The Internet of Things (IoT) is one of the promising technologies of the future. It offers many attractive features that we depend on nowadays with less effort and faster in real-time. However, it is still vulnerable to various threats and attacks due to the obstacles of its heterogeneous ecosystem, adaptive protocols, and self-configurations. In this paper, three different 6LoWPAN attacks are implemented in the IoT via Contiki OS to generate the proposed dataset that reflects the 6LoWPAN features in IoT. For analyzed attacks, six scenarios have been implemented. Three of these are free of malicious nodes, and the others scenarios include malicious nodes. The typical scenarios are a benchmark for the malicious scenarios for comparison, extraction, and exploration of the features that are affected by attackers. These features are used as criteria input to train and test our proposed hybrid Intrusion Detection and Prevention System (IDPS) to detect and prevent 6LoWPAN attacks in the IoT ecosystem. The proposed hybrid IDPS has been trained and tested with improved accuracy on both KoU-6LoWPAN-IoT and Edge IIoT datasets. In the proposed hybrid IDPS for the detention phase, the Artificial Neural Network (ANN) classifier achieved the highest accuracy among the models in both the 2-class and N-class. Before the accuracy improved in our proposed dataset with the 4-class and 2-class mode, the ANN classifier achieved 95.65% and 99.95%, respectively, while after the accuracy optimization reached 99.84% and 99.97%, respectively. For the Edge IIoT dataset, before the accuracy improved with the 15-class and 2-class modes, the ANN classifier achieved 95.14% and 99.86%, respectively, while after the accuracy optimized up to 97.64% and 99.94%, respectively. Also, the decision tree-based models achieved lightweight models due to their lower computational complexity, so these have an appropriate edge computing deployment. Whereas other ML models reach heavyweight models and are required more computational complexity, these models have an appropriate deployment in cloud or fog computing in IoT networks.
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Jiang, Xiaoming, Wouter Van den Broek, and Christoph T. Koch. "Inverse dynamical photon scattering (IDPS): an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy." Optics Express 24, no. 7 (2016): 7006. http://dx.doi.org/10.1364/oe.24.007006.
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V, Balajishanmugam, Christopher Paul A, and Thirunavukarasu B. "ENHANCED INTRUSION DETECTION AND PREVENTION IN WIRELESS SENSOR NETWORKS USING HYBRID DEEP LEARNING." ICTACT Journal on Communication Technology 16, no. 1 (2025): 3454–58. https://doi.org/10.21917/ijct.2025.0513.
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Wireless Sensor Networks (WSNs) are highly vulnerable to security threats due to their decentralized nature, constrained resources, and open communication channels. Traditional intrusion detection and prevention systems (IDPS) often struggle to provide real-time protection while maintaining network efficiency. The increasing complexity of cyberattacks necessitates advanced techniques for threat mitigation. A major challenge in WSN security is the detection of sophisticated intrusions with high accuracy while minimizing false positives and computational overhead. Conventional rule-based and anomaly-based detection methods exhibit limitations in identifying emerging threats due to their reliance on predefined signatures and static models. Addressing these gaps, a hybrid deep learning-based IDPS is proposed, integrating Convolutional Neural Networks (CNNs) for feature extraction and Long Short-Term Memory (LSTM) networks for sequential pattern learning. The hybrid model is trained on a benchmark WSN intrusion dataset and optimized using the Adam optimizer to enhance detection performance. Experimental evaluation shows that the proposed model achieves an intrusion detection accuracy of 98.6%, significantly outperforming traditional machine learning approaches such as Support Vector Machines (SVM) (91.2%) and Random Forest (94.8%). The system also reduces false positive rates to 1.8%, ensuring reliable threat identification. Moreover, real- time implementation exhibits an average detection latency of 0.35 seconds, making it suitable for resource-constrained WSN environments. These results indicate that the hybrid CNN-LSTM model effectively enhances the security of WSNs, providing a robust defense against evolving cyber threats.
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Sharma, Himanshu, Prabhat Kumar, and Kavita Sharma. "Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3778–95. http://dx.doi.org/10.12694/scpe.v25i5.3004.
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The security of Internet of Things (IoT) networks has become a integral problem in view of the exponential growth of IoT devices. Intrusion detection and prevention is an approach ,used to identify, analyze, and block cyber threats to protect IoT from unauthorized access or attacks. This paper introduces an adaptive and incremental intrusion detection and prevention system based on RNNs, to the ever changing field of IoT security. IoT networks require advanced intrusion detection systems that can identify emerging threats because of their various and dynamic data sources. The complexity of IoT network data makes it difficult for traditional intrusion detection techniques to detect potential threats. Using the capabilities of RNNs, a model for creating and deploying an intrusion detection and prevention system (IDPS) is proposed in this paper. RNNs work particularly well for sequential data processing, which makes them an appropriate choice for IoT network traffic monitoring. NSL-KDD dataset is taken, pre-processed, features are extracted, and RNN-based model is built as a part of the proposed work. The experimental findings illustrate how effective the suggested approach is at identifying and blocking intrusions in Internet of Things networks. This paper not only demonstrates the effectiveness of RNNs in enhancing IoT network security but also opens avenues for further exploration in this burgeoning field. It presents a scalable, adaptive intrusion detection and prevention solution, responding to the evolving landscape of IoT security. As IoT networks continue to expand, the research enriches the discourse on developing resilient security strategies to combat emerging threats in scalable computing environments.
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Bai, Jian, Zhuo Zhang, and Bingshen Shen. "Internet of vehicles security situation awareness based on intrusion detection protection systems." Journal of Computational Methods in Sciences and Engineering 22, no. 1 (2022): 189–95. http://dx.doi.org/10.3233/jcm-215889.
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With the rise of technologies such as mobile Internet, 5G networks and artificial intelligence, the development of Internet of Vehicle Information Security (ICVS) has become the mainstream and direction for the future development of the automotive industry. ICVS, people, roads, clouds, and APP constitute a complex network of vehicles. As part of the Internet, vehicle networking will inevitably face various complex information security threats and risks. This paper aims to design a kind of security situation awareness of Internet of vehicles based on intrusion detection protection systems (IDPS). By collecting the security data of car, app and private cloud for big data analysis, the whole smart car security situation awareness system is constructed. The system can be used to analyze potential threats, send out warnings, and carry out emergency responses.
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S, Bhaggiaraj, Shanthini S, Sugantha Mallika S.S., and Muthuram R. "NEXT-GENERATION INTRUSION DETECTION AND PREVENTION SYSTEMS FOR IT AND NETWORK SECURITY." ICTACT Journal on Communication Technology 14, no. 3 (2023): 2992–97. http://dx.doi.org/10.21917/ijct.2023.0445.
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In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devices within the IT infrastructure, ensuring the availability of labeled data that signifies known intrusion events. After meticulous preprocessing and feature engineering, we adapt the Dense VGG model, originally designed for image classification, to work with tabular IoT data. Transfer learning techniques are applied, leveraging pre-trained VGG models to expedite convergence and enhance performance. Real-time data streaming mechanisms are established to seamlessly integrate IoT data, making the system proactive in identifying threats. Upon detection, the system can respond by isolating affected devices, blocking suspicious network traffic, or initiating incident response protocols. Continuous monitoring and evaluation ensure the system reliability, with key metrics serving as indicators of its efficacy. Deployment considerations, such as scalability and redundancy, guarantee the system readiness to handle the influx of IoT data. Furthermore, integration with other security tools and compliance with regulatory standards strengthen the system overall cybersecurity posture. The core of our system lies in its intrusion detection logic, a set of rules and thresholds that trigger alerts or preventive measures based on model predictions. In testing, our system demonstrated an impressive intrusion detection accuracy of over 95%, significantly reducing false positives.
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Kayembe, Harry César, Didier Bompangue, Catherine Linard, et al. "Drivers of the dynamics of the spread of cholera in the Democratic Republic of the Congo, 2000–2018: An eco-epidemiological study." PLOS Neglected Tropical Diseases 17, no. 8 (2023): e0011597. http://dx.doi.org/10.1371/journal.pntd.0011597.
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Background The dynamics of the spread of cholera epidemics in the Democratic Republic of the Congo (DRC), from east to west and within western DRC, have been extensively studied. However, the drivers of these spread processes remain unclear. We therefore sought to better understand the factors associated with these spread dynamics and their potential underlying mechanisms. Methods In this eco-epidemiological study, we focused on the spread processes of cholera epidemics originating from the shores of Lake Kivu, involving the areas bordering Lake Kivu, the areas surrounding the lake areas, and the areas out of endemic eastern DRC (eastern and western non-endemic provinces). Over the period 2000–2018, we collected data on suspected cholera cases, and a set of several variables including types of conflicts, the number of internally displaced persons (IDPs), population density, transportation network density, and accessibility indicators. Using multivariate ordinal logistic regression models, we identified factors associated with the spread of cholera outside the endemic eastern DRC. We performed multivariate Vector Auto Regressive models to analyze potential underlying mechanisms involving the factors associated with these spread dynamics. Finally, we classified the affected health zones using hierarchical ascendant classification based on principal component analysis (PCA). Findings The increase in the number of suspected cholera cases, the exacerbation of conflict events, and the number of IDPs in eastern endemic areas were associated with an increased risk of cholera spreading outside the endemic eastern provinces. We found that the increase in suspected cholera cases was influenced by the increase in battles at lag of 4 weeks, which were influenced by the violence against civilians with a 1-week lag. The violent conflict events influenced the increase in the number of IDPs 4 to 6 weeks later. Other influences and uni- or bidirectional causal links were observed between violent and non-violent conflicts, and between conflicts and IDPs. Hierarchical clustering on PCA identified three categories of affected health zones: densely populated urban areas with few but large and longer epidemics; moderately and accessible areas with more but small epidemics; less populated and less accessible areas with more and larger epidemics. Conclusion Our findings argue for monitoring conflict dynamics to predict the risk of geographic expansion of cholera in the DRC. They also suggest areas where interventions should be appropriately focused to build their resilience to the disease.
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Chen, Tongtong, and Xiangxue Li. "(Semi-)Automatically Parsing Private Protocols for In-Vehicle ECU Communications." Entropy 23, no. 11 (2021): 1495. http://dx.doi.org/10.3390/e23111495.
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In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network.
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Meng, Di, and Gianluca Pollastri. "PUNCH2: Explore the strategy for intrinsically disordered protein predictor." PLOS ONE 20, no. 3 (2025): e0319208. https://doi.org/10.1371/journal.pone.0319208.
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Intrinsically disordered proteins (IDPs) and their intrinsically disordered regions (IDRs) lack stable three-dimensional structures, posing significant challenges for computational prediction. This study introduces PUNCH2 and PUNCH2-light, advanced predictors designed to address these challenges through curated datasets, innovative feature extraction, and optimized neural architectures. By integrating experimental datasets from PDB (PDB_missing) and fully disordered sequences from DisProt (DisProt_FD), we enhanced model performance and robustness. Three embedding strategies—One-Hot, MSA-based, and PLM-based embeddings—were evaluated, with ProtTrans emerging as the most effective single embedding and combined embeddings achieving the best results. The predictors employ a 12-layer convolutional network (CNN_L12_narrow), offering a balance between accuracy and computational efficiency. PUNCH2 combines One-Hot, ProtTrans, and MSA-Transformer embeddings, while PUNCH2-light provides a faster alternative excluding MSA-based embeddings. PUNCH2 and its streamlined variant, PUNCH2-light, are competitive with other predictors on the CAID2 benchmark and rank as the top two predictors in the CAID3 competition. These tools provide efficient, accurate solutions to advance IDP research and understanding.
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Bashynsky, V., Yu Kliat, and A. Trystan. "METHODOLOGICAL APPROACH TO FORECASTING SOURCES OF MILITARY THREATS TO ENSURING THE MILITARY SECURITY OF UKRAINE." Випробування та сертифікація, no. 3(5) (December 30, 2024): 14–19. https://doi.org/10.37701/ts.05.2024.02.
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The article proposes a methodological approach to forecasting the sources of military threats to ensure the military security of Ukraine. The methodological approach is based on forecasting the state of the international situation, the military-political situation and their interrelationships. Taking into account the trends in the development of modern civilization until 2035, it is proposed to take into account a number of key factors, namely: the influence of government structures as subjects of IDPs; the role of non-state actors; global trends in political, economic, information, communication and scientific and technological development. Two important megatrends are considered: demographic problems and the growing demand for food and resources. To solve the problem of forecasting the sources of military conflicts, a number of directions are proposed, namely: the creation of effective infrastructure; integration of modern technologies; development of a flexible management system; creation of a network of international cooperation.
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Guo, Hao-Bo, Baxter Huntington, Alexander Perminov, et al. "AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein." PLOS ONE 19, no. 5 (2024): e0301866. http://dx.doi.org/10.1371/journal.pone.0301866.
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We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.
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Pramudya, Otniel Dewangga Divan, Puspanda Hatta, and Cucuk Wawan Budiyanto. "MODELING INTRUSION DETECTION AND PREVENTION SYSTEM TO DETECT AND PREVENT NETWORK ATTACKS USING WAZUH." Jurnal Teknik Informatika (Jutif) 6, no. 1 (2025): 173–86. https://doi.org/10.52436/1.jutif.2025.6.1.1830.
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The rapid development of technology has a positive impact on society. The internet can be easily accessed anytime and anywhere, but with the advancement of internet technology, there are many threats lurking in the security of its users. Criminal activities in the digital world are referred to as cybercrime. Numerous cases of cybercrime have occurred worldwide, ranging from attacks that can disable servers to data theft and illegal access. It is noted that more than 50% of companies do not have a plan to respond to these cybercrimes. This is due to various factors, one of which is the limited availability of freely accessible and easily configurable network security platforms for all users. Therefore, this research aims to provide a solution in the form of an open-source-based Intrusion Detection and Prevention System (IDPS) that can be freely distributed and easily configured, one of which is Wazuh. The study uses the Cisco PPDIOO approach in developing a virtual lab with various scenarios for testing and measuring the Quality of Services (QoS) of Wazuh's performance. From the created test scenarios, Wazuh can detect attacks from both inside and outside the network. Wazuh has proven to be capable of detecting and preventing various types of network attacks and features that can facilitate users in responding to cybercrime, making it a potential solution for organizations that have not planned to respond to cybercrime.
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Nayana R, Harish G N, and Asharani R. "A comprehensive survey of modern network security techniques and challenges." World Journal of Advanced Research and Reviews 3, no. 2 (2019): 101–10. http://dx.doi.org/10.30574/wjarr.2019.3.2.0069.
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This review paper provides a comprehensive survey of modern network security techniques and the multifaceted challenges they address in an increasingly interconnected world. As cyber threats continue to evolve in both sophistication and frequency, organizations must deploy advanced defense mechanisms to protect sensitive data and maintain network integrity. We explore the shifting landscape of cyber threats, ranging from traditional attacks like malware and ransomware to more complex and persistent threats such as Advanced Persistent Threats (APTs) and zero-day exploits. The paper examines a range of defense strategies, including the implementation of Intrusion Detection and Prevention Systems (IDPS), which leverage both signature-based and anomaly-based detection techniques to identify malicious activities in real time. Additionally, we provide an in-depth analysis of modern encryption protocols like Transport Layer Security (TLS) and Virtual Private Networks (VPNs), which secure communication channels and protect data in transit. A significant portion of the paper is devoted to Zero-Trust Architecture (ZTA), a security model that eliminates implicit trust within a network and enforces strict verification for every access request. We discuss the principles of zero trust, its growing adoption, and the associated implementation challenges in large-scale environments. Moreover, the paper delves into the integration of Machine Learning (ML) and Artificial Intelligence (AI) in cyber security, exploring their role in threat detection, automated response systems, and the enhancement of threat intelligence. We also address the unique security challenges posed by emerging technologies such as the Internet of Things (IoT) and cloud computing, which introduce new vulnerabilities due to device heterogeneity, scalability issues, and shared responsibility models. This review outlines the current state of network security technologies, highlights key challenges in securing modern networks, and explores future trends such as quantum-resistant encryption and AI-driven automation in cybersecurity.'
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Zain ul Abideen, Muhammad, Shahzad Saleem, and Madiha Ejaz. "VPN Traffic Detection in SSL-Protected Channel." Security and Communication Networks 2019 (October 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/7924690.
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In recent times, secure communication protocols over web such as HTTPS (Hypertext Transfer Protocol Secure) are being widely used instead of plain web communication protocols like HTTP (Hypertext Transfer Protocol). HTTPS provides end-to-end encryption between the user and service. Nowadays, organizations use network firewalls and/or intrusion detection and prevention systems (IDPS) to analyze the network traffic to detect and protect against attacks and vulnerabilities. Depending on the size of organization, these devices may differ in their capabilities. Simple network intrusion detection system (NIDS) and firewalls generally have no feature to inspect HTTPS or encrypted traffic, so they rely on unencrypted traffic to manage the encrypted payload of the network. Recent and powerful next-generation firewalls have Secure Sockets Layer (SSL) inspection feature which are expensive and may not be suitable for every organizations. A virtual private network (VPN) is a service which hides real traffic by creating SSL-protected channel between the user and server. Every Internet activity is then performed under the established SSL tunnel. The user inside the network with malicious intent or to hide his activity from the network security administration of the organization may use VPN services. Any VPN service may be used by users to bypass the filters or signatures applied on network security devices. These services may be the source of new virus or worm injected inside the network or a gateway to facilitate information leakage. In this paper, we have proposed a novel approach to detect VPN activity inside the network. The proposed system analyzes the communication between user and the server to analyze and extract features from network, transport, and application layer which are not encrypted and classify the incoming traffic as malicious, i.e., VPN traffic or standard traffic. Network traffic is analyzed and classified using DNS (Domain Name System) packets and HTTPS- (Hypertext Transfer Protocol Secure-) based traffic. Once traffic is classified, the connection based on the server’s IP, TCP port connected, domain name, and server name inside the HTTPS connection is analyzed. This helps in verifying legitimate connection and flags the VPN-based traffic. We worked on top five freely available VPN services and analyzed their traffic patterns; the results show successful detection of the VPN activity performed by the user. We analyzed the activity of five users, using some sort of VPN service in their Internet activity, inside the network. Out of total 729 connections made by different users, 329 connections were classified as legitimate activity, marking 400 remaining connections as VPN-based connections. The proposed system is lightweight enough to keep minimal overhead, both in network and resource utilization and requires no specialized hardware.
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Mironeanu, Cătălin, Alexandru Archip, Cristian-Mihai Amarandei, and Mitică Craus. "Experimental Cyber Attack Detection Framework." Electronics 10, no. 14 (2021): 1682. http://dx.doi.org/10.3390/electronics10141682.
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Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.
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Huang, Jingfeng, Istvan Laszlo, Lorraine A. Remer, et al. "Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm." Atmospheric Measurement Techniques 11, no. 10 (2018): 5813–25. http://dx.doi.org/10.5194/amt-11-5813-2018.
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Abstract. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft provides validated daily global aerosol optical depth (AOD) retrievals; however, a close examination of the VIIRS aerosol product identified residual snow and snowmelt contamination, resulting generally in an overestimation of AOD. The contamination was particularly evident over Northern Hemisphere high-latitude regions during the spring thaw. To improve the product performance, we introduced a new empirical snow and snowmelt screening scheme that combines a normalized difference snow index (NDSI)- and brightness temperature (BT)-based snow test, a snow adjacency test and a spatial homogeneity test (a.k.a. spatial filter). Testing of retrievals for 18 May 2014 indicated that, compared to the previous, visible reflectance anomaly (VRA)-based snow test, the new NDSI- and BT-based snow test screened out an additional 3.44 % of VIIRS AOD retrievals, most of which were over high latitudes experiencing snowmelt. The new snow adjacency test and the homogeneity test degraded another 5.57 % and 0.26 %, respectively, otherwise “good”-quality AOD retrievals. For the VIIRS–AERONET (Aerosol Robotic Network) matchups over Northern Hemisphere high-latitude regions during 3 years of spring (2013–2015), the new scheme also effectively screened out a significant number of the matchups that had anomalously high positive biases attributable to snow and snowmelt contamination. The new snow and snowmelt screening scheme was transferred to the Interface Data Processing Segment (IDPS) VIIRS aerosol algorithm on 22 June 2015. Subsequently no significant snow and snowmelt contamination was found during spring 2016. The scheme is also implemented in the new Enterprise VIIRS aerosol algorithm in the National Oceanic and Atmospheric Administration (NOAA) Enterprise Processing System (EPS) that became operational in 2017.
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Karmous, Neder, Mohamed Ould-Elhassen Aoueileyine, Manel Abdelkader, Lamia Romdhani, and Neji Youssef. "Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices." Sensors 24, no. 15 (2024): 5022. http://dx.doi.org/10.3390/s24155022.
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The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people’s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices’ security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.
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Onyechere, Udoka, Toibat Adeyinka, Eghonghon Abumere, and Ada Ugochukwu. "Providing Virtual Psychosocial and Trauma Support to Displaced Women in Nigeria: A Case Study of Courage, Innovation and Collaboration; Core Values and Behaviours of the Royal College of Psychiatrists." BJPsych Open 11, S1 (2025): S313—S314. https://doi.org/10.1192/bjo.2025.10753.
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Aims: Internally displaced persons (IDPs) in Nigeria experience numerous challenges, including psychological distress resulting from displacement and trauma. In October 2024, a cohort of volunteer mental health professionals based in the United Kingdom provided sessions of mental health support via virtual means to displaced women in Abuja, Nigeria. This intervention utilised telemedicine to address barriers to access and adhered to the principles of Psychological First Aid (PFA).Methods: The initiative required significant pre-event coordination. Mental health professionals from local and international backgrounds were recruited to volunteer, and a structured counselling protocol was developed. Zoom conferencing was utilised to conduct the sessions, with efforts made to incorporate trauma-informed approaches. Despite thorough planning, technical difficulties arose, including unreliable Wi-Fi connections that intermittently disrupted sessions. Power outages were also problematic, necessitating the use of generators at the Nigerian location to maintain a supportive environment. During the sessions, participants disclosed accounts of hardship and trauma. Volunteers emphasised active listening, empathy, and cultural sensitivity to establish trust and provide meaningful support.Results: Collaboration played a pivotal role in the success of this initiative, showcasing its critical contribution to the field of mental health care. The collaboration between local and international mental health professionals underscored the strength of teamwork. Each volunteer’s dedication, despite the challenges, contributed to the overall impact of the project. The shared responsibility among the team fostered a supportive network that bolstered resilience against challenges, which is key in mental health-care provision. Feedback from participants and volunteers reflected the success of the initiative, with attendees expressing gratitude and an openness to future mental health interventions.Conclusion: This initiative demonstrated the power of perseverance, creativity, and collaborative efforts in delivering psychosocial support to underserved populations. By adopting a flexible approach and prioritising the needs of the participants, the event successfully bridged gaps in care and provided a platform for displaced women to be heard and supported. Future initiatives can build upon these findings, enhancing technological resilience and refining to ensure greater reach to those most in need.
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D, Rajagopal, and Padmanabhan K. "Artificial Intelligence Based Multi-Layer Approach for Finding Unknown Attacks in Cloud Network by Using Hybrid Intrusion Detection." Indian Journal of Science and Technology 17, no. 35 (2024): 3643–52. https://doi.org/10.17485/IJST/v17i35.2209.
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Abstract <strong>Objectives :</strong> The objective of this study is to explore Intrusion Detection Systems and their various types by gathering research from previously published articles in refereed journals. The focus is on developing a proposed model capable of identifying unknown attacks in cloud networks using Signature and Anomaly-based Intrusion Detection Systems. Subsequently, the efficiency of the proposed model will be assessed, and a comparison with existing models will be conducted. The paper's main objective is to identify unknown attacks in a cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach. <strong>Methods:</strong> Leveraging insights from existing literature, the proposed model combines signature-based IDS for known threat detection and anomaly-based IDS for detecting unusual behavioral patterns indicative of new or unseen attacks. Experimental evaluations using NSL-KDD and ADFA datasets demonstrate competitive accuracy and detection rates, with the proposed artificial intelligence-based Hybrid IDS achieving high performance in detecting both normal and malicious activities. <strong>Findings:</strong> This model produces above 90%, 96%, and 98% efficiency in the wired, Wireless, and Cloud networks respectively, and this model finds known attacks effectively while using parameters like event logs, file transferring time, TCP and UDP addresses, CPU Usage, Weak and synthetic data, IP and MAC address. Existing literature said that the existing model using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The findings suggest that the artificial intelligence-based multi-layered approach offers a promising solution for enhancing cloud network security, with the potential for further optimization and integration of advanced technologies in future research endeavors. <strong>Novelty:</strong> This study presents an artificial intelligence-based multi-layered approach for detecting unknown attacks in cloud networks by integrating signature-based and anomaly-based intrusion detection systems (IDS). The authors developed the model to detect the intrusion by using the Behaviour Profiling algorithm and dynamically prevent the data from intrusion by using the Statistical approach model. The authors trying to find unknown attacks, therefore the authors defined the objective of this paper as to find the unknown attacks in cloud networks by using the combination of signature and anomaly-based intrusion detection systems. The objective is to develop a model capable of effectively identifying cyber threats in cloud environments. The existing models do not concentrate on identifying unknown attacks by using Signature-based Intrusion Detection. Very few of the literature said that known attacks can be identified easily by using Signature-based Intrusion Detection but the unknown attacks identifying process is hard. Some of the Literature said that using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The current paper identifies unknown attacks in the cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach and it produced above 97% accuracy. The unique feature of the model is artificial intelligence-based multi-layered approach and dynamic key have been utilized to avoid malicious activities in the network. <strong>Keywords:</strong> Anomaly based IDS, Cloud Network Security, Hybrid IDS, Multi-Layer Approach, Signature based IDS
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Liu, Xiaoguang, Huanliang Li, Cunguang Lou, Tie Liang, Xiuling Liu, and Hongrui Wang. "A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network." Sensors 19, no. 12 (2019): 2814. http://dx.doi.org/10.3390/s19122814.
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Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.
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Yan, Binghao, and Guodong Han. "LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network." Security and Communication Networks 2018 (August 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/6026878.
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The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.
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Bassam, Hasan, Alani Sameer, and Ayad Saad Mohammed. "Secured node detection technique based on artificial neural network for wireless sensor network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 536–44. https://doi.org/10.11591/ijece.v11i1.pp536-544.
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The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.
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D., Parameswari, and Khanaa V. "Network Based Intrusion Detection System using Protocol Standardization Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 191–94. https://doi.org/10.35940/ijeat.B3351.129219.
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The IDS system identifies the anomaly device which connected in the network communication process through evaluating the MAC address compared with the registered list of devices. In completion, this research work ensures that all the devices which are involved in the network communications are authenticated and secured, which increases the security of the network and prevents the intruder. This research work attempts to increase the quality of service of network communication, ensuring error-free communication through monitoring the network.
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Clavijo-López, Rosa, Dr Wayky Alfredo Luy Navarrete, Dr Jesús Merino Velásquez, Dr Carlos Miguel Aguilar Saldaña, Alcides Muñoz Ocas, and Dr César Augusto Flores Tananta. "Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems." Journal of Internet Services and Information Security 14, no. 1 (2024): 206–18. http://dx.doi.org/10.58346/jisis.2024.i1.014.
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Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.
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Jo, Dae Woong, and Myung Ho Kim. "Design and Implementation for the Secure ID Management System Based on Virtualization." Applied Mechanics and Materials 284-287 (January 2013): 3390–94. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3390.
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In this paper, we study a solution to solve the authentication problem in the ID management system (IDMS). Our solution is based on the virtualization technology which allows data transfer between two servers in the absence of the network connection. In this paper, we propose a Virtual ID management system (VIDMS) that logically divides the login server and the authentication server in a physical system. The ID authentication process minimizes the security threat in the network since it can be performed using the virtual shared memory in the absence of the network connection. As compared with the existing IDMS, our solution improves the authentication performance (in terms of the speed).
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Jumabek, Alikhanov, SeungSam Yang, and YoungTae Noh. "CatBoost-Based Network Intrusion Detection on Imbalanced CIC-IDS-2018 Dataset." Journal of Korean Institute of Communications and Information Sciences 46, no. 12 (2021): 2191–97. http://dx.doi.org/10.7840/kics.2021.46.12.2191.
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Jyoti, Snehi, Bhandari Abhinav, Baggan Vidhu, and Snehi Ritu Manish. "Diverse Methods for Signature based Intrusion Detection Schemes Adopted." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 44–49. https://doi.org/10.35940/ijrte.A2791.079220.
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Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature-based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each intrusion detection system feature, system administrators may help identify cyber-attacks and threats to the network and Computer system. Eighty percent of incidents can be easily and promptly detected using signature-based detection methods if used as a precautionary phase for vulnerability detection and twenty percent rest by anomaly-based intrusion detection system that involves comparing definitions of normal activity or event behavior with observed events in identifying the significant deviations and deciding the traffic to flag.
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LI, L., M. ZAHEERUDDIN, SUNG-HWAN CHO, and SANG-HOON JUNG. "STEADY STATE AND DYNAMIC MODELING OF AN INDIRECT DISTRICT HEATING SYSTEM." International Journal of Air-Conditioning and Refrigeration 18, no. 01 (2010): 61–75. http://dx.doi.org/10.1142/s2010132510000083.
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An indirect district heating system (IDHS) with heated floor area of 851 031 m2 and ten heat exchange stations was modeled in this study. An aggregated steady state model for the system was developed to study the impact of important system parameters. A dynamic model of the IDHS was developed based on energy balance principles. The dynamic model consists of sub-system models such as boiler, pipe network, heat exchanger, terminal heater and zone models. Simulation results of the dynamic responses show that the overall efficiency of the IDHS system is 78.7%, and the two highest heat loss components are the boiler heat losses and the secondary water makeup loss.
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Abu Al-Haija, Qasem, and Saleh Zein-Sabatto. "An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks." Electronics 9, no. 12 (2020): 2152. http://dx.doi.org/10.3390/electronics9122152.
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With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.
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Li, Xiaoya, Chenyang Zhang, Zhoujin Tan, and Jiali Yuan. "Network Pharmacology-Based Analysis of Gegenqinlian Decoction Regulating Intestinal Microbial Activity for the Treatment of Diarrhea." Evidence-Based Complementary and Alternative Medicine 2021 (July 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/5520015.
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Gegenqinlian decoction (GD) has been extensively used for the treatment of diarrhea with intestinal dampness-heat syndrome (IDHS) with a satisfying therapeutic effect. The purpose of this study is to clarify the active ingredients and mechanism of GD in the treatment of diarrhea with IDHS. The TCMSP database was used to screen out the active ingredients of the four Chinese herbal medicines in GD, and the targets of the active ingredients were predicted. We selected the targets related to diarrhea through the DisGeNET database, then used the NCBI database to screen out related targets of lactase and sucrase, and constructed the visual network to search for the active ingredients of GD in the treatment of diarrhea and related mechanisms of the targets. Combined with network pharmacology, we screened out 146 active ingredients in GD corresponding to 252 ingredient targets, combined with 328 disease targets in diarrhea, and obtained 12 lactase targets and 11 sucrase targets. The key active ingredients involved quercetin, formononetin, β-sitosterol kaempferol, and wogonin. Furthermore, molecular docking showed that these five potential active ingredients had good affinities with the core targets PTGS2. The active ingredients in GD (such as quercetin, formononetin, and β-sitosterol) may increase the microbial activity of the intestinal mucosa of mice and reduce the microbial activity of the intestinal contents through multiple targets, thereby achieving the effect of treating diarrhea.
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Gao, Ze. "A review on statistical language and neural network based code completion." Applied and Computational Engineering 22, no. 1 (2023): 233–39. http://dx.doi.org/10.54254/2755-2721/22/20231222.
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Code completion, also referred to as intellisense, is a prevalent feature of Integrated Development Environments (IDEs) and code editors. It aids developers by automatically recommending and inserting code segments, variable names, and method names, among other things. With the accelerated growth of the software industry and the process of digitalization in recent years, the demand for software engineers has reached a record-high level. Thus, the advancement of code completion is encouraged and has become a popular topic in software engineering. This paper examines and summarizes the development of a statistical language and neural network-based code completion system. The main contents consist of introducing the concepts of code completion system, summarizing the general process of code completion and the evaluation metrics used for performance benchmarking, reviewing and summarizing the existing work conducted on statistical language approach and neural network approach respectively, as well as the limitations and challenges of existing code completion method, and finally forecasting the future development of code completion techniques.
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Sinha, Subrata, Sanchita Sharma, Surabhi Johari, Ashwani Sharma, and Sanchaita Rajkhowa. "Design and Development of Hydrophobicity and Net charge Based Artificial Neural Network Model for IDP/IDPR Prediction." Procedia Computer Science 218 (2023): 438–48. http://dx.doi.org/10.1016/j.procs.2023.01.026.
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Ji, Ram, Neerendra Kumar, and Devanand Padha. "Hybrid Enhanced Intrusion Detection Frameworks for Cyber-Physical Systems via Optimal Features Selection." Indian Journal Of Science And Technology 17, no. 30 (2024): 3069–79. http://dx.doi.org/10.17485/ijst/v17i30.1794.
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Background/Objectives: Cyber-physical systems (CPSs) form the critical infrastructure for many nations like smart grids, home automation, smart cities, smart health care, smart automobiles, etc. These systems are susceptible to various attacks due to their wider surface area. Cyber-attacks on these systems can interrupt the critical services provided by them. Thus, intrusion detection frameworks (IDFs) are needed to identify the attacks on CPSs so that countermeasures can be taken to minimize the harm of such attacks. Limitations of existing IDFs are poor detection rate, high detection time, high false alarm rate, and large space and time complexities. The objective of this study is to design hybrid-enhanced IDFs to overcome these issues. Methods: Two enhanced IDFs are proposed in this research work. SelectKBest-MI (mutual information), framework fuses two filter-based feature selection techniques namely SelectKBest and mutual information for selecting optimal features, and the Random Forest (RF) is utilized as a classifier. The second proposed IDF is named CNN-SVM-GWO. Convolutional Neural Network (CNN) is used for extraction of attributes, Support Vector Machine (SVM) and Gray Wolf Optimizer (GWO) are used for the optimal number of feature selection, RF and Extreme Gradient Boosting (XGB) classifiers are used for intrusion detection. Two datasets have been used: CICIDS2017 and CIC-IoT-2023. Parameters considered for comparison with existing techniques are accuracy, precision, recall, F1-score, and prediction time. Findings: Implementation of SelectKBest-MI framework using the CICIDS2017 dataset, results in better accuracy of 99.99%, precision of 0.99, recall of 0.99, F1-score 0.99 for binary classification. Implementation of CNN-SVM-GWO framework using CIC-IoT-2023 dataset results in accuracy 99.60%(RF), 99.49(XGB), precision 0.99, recall 0.99, F1-score 0.99. Novelty: CNN-SVM-GWO IDF prediction time is 0.75 seconds (RF) and 0.078 seconds (XGB). The proposed model has reduced time complexity. Novel hybrid IDFs for optimal feature selection are proposed with enhanced efficiency. Keywords: Cyber-physical systems, Intrusion detection system, Optimal feature selection, Gray wolf optimizer, Convolutional neural network
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Ram, Ji, Kumar Neerendra, and Padha Devanand. "Hybrid Enhanced Intrusion Detection Frameworks for Cyber-Physical Systems via Optimal Features Selection." Indian Journal of Science and Technology 17, no. 30 (2024): 3069–79. https://doi.org/10.17485/IJST/v17i30.1794.
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Abstract <strong>Background/Objectives:</strong> Cyber-physical systems (CPSs) form the critical infrastructure for many nations like smart grids, home automation, smart cities, smart health care, smart automobiles, etc. These systems are susceptible to various attacks due to their wider surface area. Cyber-attacks on these systems can interrupt the critical services provided by them. Thus, intrusion detection frameworks (IDFs) are needed to identify the attacks on CPSs so that countermeasures can be taken to minimize the harm of such attacks. Limitations of existing IDFs are poor detection rate, high detection time, high false alarm rate, and large space and time complexities. The objective of this study is to design hybrid-enhanced IDFs to overcome these issues. <strong>Methods:</strong> Two enhanced IDFs are proposed in this research work. SelectKBest-MI (mutual information), framework fuses two filter-based feature selection techniques namely SelectKBest and mutual information for selecting optimal features, and the Random Forest (RF) is utilized as a classifier. The second proposed IDF is named CNN-SVM-GWO. Convolutional Neural Network (CNN) is used for extraction of attributes, Support Vector Machine (SVM) and Gray Wolf Optimizer (GWO) are used for the optimal number of feature selection, RF and Extreme Gradient Boosting (XGB) classifiers are used for intrusion detection. Two datasets have been used: CICIDS2017 and CIC-IoT-2023. Parameters considered for comparison with existing techniques are accuracy, precision, recall, F1-score, and prediction time. <strong>Findings:</strong> Implementation of SelectKBest-MI framework using the CICIDS2017 dataset, results in better accuracy of 99.99%, precision of 0.99, recall of 0.99, F1-score 0.99 for binary classification. Implementation of CNN-SVM-GWO framework using CIC-IoT-2023 dataset results in accuracy 99.60%(RF), 99.49(XGB), precision 0.99, recall 0.99, F1-score 0.99. <strong>Novelty:</strong> CNN-SVM-GWO IDF prediction time is 0.75 seconds (RF) and 0.078 seconds (XGB). The proposed model has reduced time complexity. Novel hybrid IDFs for optimal feature selection are proposed with enhanced efficiency. <strong>Keywords:</strong> Cyber-physical systems, Intrusion detection system, Optimal feature selection, Gray wolf optimizer, Convolutional neural network
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Kaye, Deborah R., Hye Sung Min, Edward C. Norton, et al. "System-Level Health-Care Integration and the Costs of Cancer Care Across the Disease Continuum." Journal of Oncology Practice 14, no. 3 (2018): e149-e157. http://dx.doi.org/10.1200/jop.2017.027730.
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Purpose: Policy reforms in the Affordable Care Act encourage health care integration to improve quality and lower costs. We examined the association between system-level integration and longitudinal costs of cancer care. Methods: We used linked SEER-Medicare data to identify patients age 66 to 99 years diagnosed with prostate, bladder, esophageal, pancreatic, lung, liver, kidney, colorectal, breast, or ovarian cancer from 2007 to 2012. We attributed each patient to one or more phases of care (ie, initial, continuing, and end of life) according to time from diagnosis until death or end of study interval. For each phase, we aggregated all claims with the primary cancer diagnosis and identified patients treated in an integrated delivery network (IDN), as defined by the Becker Hospital Review list of the top 100 most integrated health delivery systems. We then determined if care provided in an IDN was associated with decreased payments across cancers and for each individual cancer by phase and across phases. Results: We identified 428,300 patients diagnosed with one of 10 common cancers. Overall, there were no differences in phase-based payments between IDNs and non-IDNs. Average adjusted annual payments by phase for IDN versus non-IDNs were as follows: initial, $14,194 versus $14,421, respectively ( P = .672); continuing, $2,051 versus $2,099 ( P = .566); and end of life, $16,257 versus $16,232 ( P = .948). However, in select cancers, we observed lower payments in IDNs. For bladder cancer, payments at the end of life were lower for IDNs ($11,041 v $12,331; P = .008). Of the four cancers with the lowest 5-year survival rates (ie, pancreatic, lung, esophageal, and liver), average expenditures during the initial and continuing-care phases were lower for patients with liver cancer treated in IDNs. Conclusion: For patients with one of 10 common malignancies, treatment in an IDN generally is not associated with lower costs during any phase of cancer care.