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Artykuły w czasopismach na temat „Attack Detection Automation”

Autor: Grafiati
Data publikacji: 16 listopada 2024
Data aktualizacji: 31 lipca 2025

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Wressnegger, Christian. "Efficient machine learning for attack detection." it - Information Technology 62, no. 5-6 (2020): 279–86. http://dx.doi.org/10.1515/itit-2020-0015.

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AbstractDetecting and fending off attacks on computer systems is an enduring problem in computer security. In light of a plethora of different threats and the growing automation used by attackers, we are in urgent need of more advanced methods for attack detection. Manually crafting detection rules is by no means feasible at scale, and automatically generated signatures often lack context, such that they fall short in detecting slight variations of known threats.In the thesis “Efficient Machine Learning for Attack Detection” [35], we address the necessity of advanced attack detection. For the
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Beshah, Yonas Kibret, Surafel Lemma Abebe, and Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System." Electronics 13, no. 6 (2024): 1004. http://dx.doi.org/10.3390/electronics13061004.

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Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic enviro
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Okello, Fredrick Ochieng, Dennis Kaburu, and Ndia G. John. "Automation-Based User Input Sql Injection Detection and Prevention Framework." Computer and Information Science 16, no. 2 (2023): 51. http://dx.doi.org/10.5539/cis.v16n2p51.

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Autodect framework protects management information systems (MIS) and databases from user input SQL injection attacks. This framework overcomes intrusion or penetration into the system by automatically detecting and preventing attacks from the user input end. The attack intentions is also known since            
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Vennapureddy, Rajasree, and T. Srinivasulu. "Pragmatic Study of Botnet Attack Detection In An IoT Environment." E3S Web of Conferences 591 (2024): 09012. http://dx.doi.org/10.1051/e3sconf/202459109012.

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A comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several databases. Studies that described the application of deep learning (DL) and machine learning (ML) methods for IoT botnet attack detection. Numerous facets of contemporary life have been transformed by the Internet of Things (IoT), including home automation, industrial control systems, healthcare, and transportation. On the other hand, as more device
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Housh, Mashor, Noy Kadosh, and Jack Haddad. "Detecting and Localizing Cyber-Physical Attacks in Water Distribution Systems without Records of Labeled Attacks." Sensors 22, no. 16 (2022): 6035. http://dx.doi.org/10.3390/s22166035.

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Modern water distribution systems (WDSs) offer automated controls and operations to improve their efficiency and reliability. Nonetheless, such automation can be vulnerable to cyber-attacks. Therefore, various approaches have been suggested to detect cyber-attacks in WDSs. However, most of these approaches rely on labeled attack records which are rarely available in real-world applications. Thus, for a detection model to be practical, it should be able to detect and localize events without referring to a predetermined list of labeled attacks. This study proposes a semi-supervised approach that
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Ye, Shengke, Kaiye Dai, Guoli Fan, Ling Zhang, and Zhihao Liang. "Exploring the intersection of network security and database communication: a PostgreSQL Socket Connection case study." Transactions on Computer Science and Intelligent Systems Research 3 (April 10, 2024): 1–9. http://dx.doi.org/10.62051/pzqebt34.

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In this study, the network security of PostgreSQL database using Socket connection is deeply analyzed. By exploring Socket connections established by PostgreSQL over TCP, we find potential security threats and vulnerabilities during data transmission, which may expose database systems to network attacks such as unauthorized access and data leakage. In order to assess these security risks, this study simulated a variety of network attack scenarios, especially the implantation and detection of Webshell, to reveal the vulnerability of PostgreSQL to such network threats. Especially in defending ag
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Karthik Krishnan, T., S. Sridevi, G. Bindu, and R. Anandan. "Comparison and detail study of attacks and detection methods for wireless sensor network." International Journal of Engineering & Technology 7, no. 2.21 (2018): 405. http://dx.doi.org/10.14419/ijet.v7i2.21.12453.

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Wireless sensor network is emanating technology in the field of telecommunications. WSNs can be applied in many fields like machine surveillance, precision agriculture, home automation and intelligent building environments. However the major aspect of WSN is the security as the sensor nodes are limited because of these facing several security threats such as black hole attack, worm hole attack, flooding etc. which is finally affecting the functioning of the whole network. These attacks are maximizing the consumption of power in the node and also it decreases life of the battery. In this paper,
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Binbusayyis, Adel. "Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers." Mathematics 12, no. 11 (2024): 1720. http://dx.doi.org/10.3390/math12111720.

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In the modern world, the evolution of the internet supports the automation of several tasks, such as communication, education, sports, etc. Conversely, it is prone to several types of attacks that disturb data transfer in the network. Efficient attack detection is needed to avoid the consequences of an attack. Traditionally, manual attack detection is limited by human error, less efficiency, and a time-consuming mechanism. To address the problem, a large number of existing methods focus on several techniques for better efficacy in attack detection. However, improvement is needed in significant
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Sztyber-Betley, Anna, Michał Syfert, Jan Maciej Kościelny, and Zuzanna Górecka. "Controller Cyber-Attack Detection and Isolation." Sensors 23, no. 5 (2023): 2778. http://dx.doi.org/10.3390/s23052778.

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This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named “cybernetic faults” that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its
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Almedires, Motaz Abdulaziz, Ahmed Elkhalil, and Mohammed Amin. "Adversarial Attack Detection in Industrial Control Systems Using LSTM-Based Intrusion Detection and Black-Box Defense Strategies." Journal of Cyber Security and Risk Auditing 2025, no. 3 (2025): 4–22. https://doi.org/10.63180/jcsra.thestap.2025.3.2.

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In industrial control systems (ICS), neural networks are increasingly being utilized to detect intrusions. The term ICS refers to a group of controlling technology and associated equipment that includes the devices, systems, networks, and controllers that are used to manage and/or execute manufacturing processes. Each ICS is developed to successfully handle work digitally and operates differently depending on the business. ICS devices and procedures are now found in practically every industry sector and key infrastructure, including production, transportation, power, and treatment plants. To a
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De La Cruz, Arvin, Florante Sangrenes, Glen Maquiran, Jonicio Dacuya, Davie Rose Taya-an, and Rey Oronos Jr. "Machine Learning-Enabled Detection of Remote Manipulation Attacks on Integrated Circuits in Hybrid AGV-Drone Systems." Technologique: A Global Journal on Technological Developments and Scientific Innovations 4, no. 1 (2025): 1–18. https://doi.org/10.62718/vmca.tech-gjtdsi.3.1.sc-0125-020.

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Hybrid autonomous guided vehicle (AGV) and drone systems represent a significant advancement in industrial automation, yet their integrated circuits (ICs) face critical cybersecurity vulnerabilities. Their interconnected IC components create expanded attack surfaces vulnerable to sophisticated cyber-attacks that enable covert remote control. This research aims to develop and validate a machine learning-enabled (ML-enabled) detection system for identifying and preventing unauthorized access attempts targeting the interconnected IC components of hybrid AGV-drone platforms. Our methodology implem
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Kim, Ye-Eun, Yea-Sul Kim, and Hwankuk Kim. "Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network." Sensors 22, no. 10 (2022): 3819. http://dx.doi.org/10.3390/s22103819.

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The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large
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Oruganti, Rakesh, Jeeshitha J, and Rama Koteswara Rao G. "A Extensive Study on DDosBotnet Attacks in Multiple Environments Using Deep Learning and Machine Learning Techniques." ECS Transactions 107, no. 1 (2022): 15181–93. http://dx.doi.org/10.1149/10701.15181ecst.

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Every organization provides security for their systems, servers, and other I.T. infrastructure resources using regular anti-viruses and malware detection software. With the increase of access to smart devices and appliances through secured and unsecured networks, there is a requirement to design an intelligent detection tool using deep learning techniques to handle complex vulnerabilities efficiently. The system should have the capability to prevent and control attacks from unreliable sources. The system administrator should immediately notify the system administrator—the proposed research stu
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Leal Piedrahita, Erwin Alexander. "Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations." Ciencia e Ingeniería Neogranadina 30, no. 1 (2019): 75–88. http://dx.doi.org/10.18359/rcin.4236.

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The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example, in the field of security, several academic works have shown that the same attacks used in computer networks (DoS, Sniffing, Tampering, Spoffing among others), can also compromise the operation of a substation. This article evaluates the applicability of hierarchical clustering algorithms and statisti
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Alotaibi, Nouf Saeed, Hassan Ibrahim Ahmed, and Samah Osama M. Kamel. "Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City." Sensors 23, no. 16 (2023): 7135. http://dx.doi.org/10.3390/s23167135.

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The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city. The proposed model consists of two pha
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Aslam, Muhammad Muzamil, Zahoor Ahmed, Liping Du, Muhammad Zohaib Hassan, Sajid Ali, and Muhammad Nasir. "An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks." Computational Intelligence and Neuroscience 2022 (August 2, 2022): 1–26. http://dx.doi.org/10.1155/2022/6732343.

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Consensus control of multiagent systems (MASs) has been one of the most extensive research topics in the field of robotics and automation. The information sharing among the agents in the MASs depends upon the communication network because the interaction of agents may affect the consensus performance of the agents in a communication network. An unexpected fault and attack may occur on one agent and can propagate through the communication network into other agents. Thus, this may cause severe degradation of the whole MASs. In this paper, we first discussed MAS technologies. After that available
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Htwe, Chaw Su, Zin Thu Thu Myint, and Yee Mon Thant. "IoT Security Using Machine Learning Methods with Features Correlation." Journal of Computing Theories and Applications 2, no. 2 (2024): 151–63. http://dx.doi.org/10.62411/jcta.11179.

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The Internet of Things (IoT) is an innovative technology that makes our environment smarter, with IoT devices as an integral part of home automation. Smart home systems are becoming increasingly popular as an IoT service in the home that connects via a network. Due to the security weakness of many devices, the malware is targeting IoT devices. After being infected with malicious attacks on smart devices, they act like bots that the intruders can control. Machine learning methods can assist in improving the attack detection process for these devices. However, the irrelevant features raise the c
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Alkahtani, Hasan, and Theyazn H. H. Aldhyani. "Developing Cybersecurity Systems Based on Machine Learning and Deep Learning Algorithms for Protecting Food Security Systems: Industrial Control Systems." Electronics 11, no. 11 (2022): 1717. http://dx.doi.org/10.3390/electronics11111717.

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Industrial control systems (ICSs) for critical infrastructure are extensively utilized to provide the fundamental functions of society and are frequently employed in critical infrastructure. Therefore, security of these systems from cyberattacks is essential. Over the years, several proposals have been made for various types of cyberattack detection systems, with each concept using a distinct set of processes and methodologies. However, there is a substantial void in the literature regarding approaches for detecting cyberattacks in ICSs. Identifying cyberattacks in ICSs is the primary aim of t
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NIKLEKAJ, Malvina, and Elfat MEMAJ. "Analyzing and Mitigating Distributed Denial-of-Service (DDoS) Attacks - A Python-Based Simulation Approach." INGENIOUS 5, no. 1 (2025): 20–36. https://doi.org/10.58944/adif5937.

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The increasing prevalence of Distributed Denial of Service (DDoS) attacks poses a significant threat to the security and availability of online services and networks. These attacks leverage multiple compromised systems to overwhelm a target, rendering it inaccessible to legitimate users. This research presents an in-depth analysis of DDoS attack methodologies, their classification into volumetric, protocol-based, and application-layer attacks, and their real-world implications. To enhance understanding and mitigation strategies, this study introduces a Python-based simulation tool that replica
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Oluwakemi, Oduwole Omolara, Muhammad, Umar Abdullahi, and Kene Tochukwu Anyachebelu. "Comparative Evaluation of Machine Learning Algorithms for Intrusion Detection." Asian Journal of Research in Computer Science 16, no. 4 (2023): 8–22. http://dx.doi.org/10.9734/ajrcos/2023/v16i4366.

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This study undertakes a comparative examination of machine learning algorithms used for intrusion detection, addressing the escalating challenge of safeguarding networks from malicious attacks in an era marked by a proliferation of network-related applications. Given the limitations of conventional security tools in combatting intrusions effectively, the adoption of machine learning emerges as a promising avenue for bolstering detection capabilities. The research evaluates the efficacy of three distinct machine learning algorithms—Convolutional Neural Networks (CNN), Recurrent Neural Networks
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Khalid, Nashmia, Sadaf Hina, Khurram Shabih Zaidi, Tarek Gaber, Lee Speakman, and Zainab Noor. "An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks." PLOS ONE 20, no. 1 (2025): e0306747. https://doi.org/10.1371/journal.pone.0306747.

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The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retain
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Noor, Hisham Kamis, Yassin Warusia, Faizal Abdollah Mohd, Fatimah Abdul Razak Siti, and Yogarayan Sumendra. "Blackhole attacks in internet of things networks: a review." Blackhole attacks in internet of things networks: a review 30, no. 2 (2023): 1080–90. https://doi.org/10.11591/ijeecs.v30.i2.pp1080-1090.

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The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low powe
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Choi, In-Sun, Junho Hong, and Tae-Wan Kim. "Multi-Agent Based Cyber Attack Detection and Mitigation for Distribution Automation System." IEEE Access 8 (2020): 183495–504. http://dx.doi.org/10.1109/access.2020.3029765.

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DAMANIK, HILLMAN AKHYAR, and MERRY ANGGRAENI. "Pola Pengelompokan dan Pencegahan Public Honeypot menggunakan Teknik K-Means dan Automation Shell-Script." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 1 (2024): 65. http://dx.doi.org/10.26760/elkomika.v12i1.65.

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ABSTRAKMakalah ini mengimplementasikan sistem log honeypot untuk menganalisis eksploitasi dari global internet berupa kategori serangan Statistical Traffic Analysis, Top Targeted Attack Sources and Destination, Penetration Analysis dan Infection Pattern Analysis serta Intrusion Detection System (IDS). Pengelompokan level kategori serangan adalah low, medium, dan high, dengan Teknik K-Means dan menerapkan rule filtering IPTables Automation yang digunakan untuk teknik mitigasi pada perangkat farm server dan virtual router public. Hasil attribute yang di cluster mendapatkan jumlah kuadrat jarak c
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Thompson, Aderonke, and Jani Suomalainen. "GAOR: Genetic Algorithm-Based Optimization for Machine Learning Robustness in Communication Networks." Network 5, no. 1 (2025): 6. https://doi.org/10.3390/network5010006.

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Machine learning (ML) promises advances in automation and threat detection for the future generations of communication networks. However, new threats are introduced, as adversaries target ML systems with malicious data. Adversarial attacks on tree-based ML models involve crafting input perturbations that exploit non-smooth decision boundaries, causing misclassifications. These so-called evasion attacks are imperceptible, as they do not significantly alter the input data distribution and have been shown to degrade the performance of tree-based models across various tasks. Adversarial training a
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Alshamsi, Omar, Khaled Shaalan, and Usman Butt. "Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach." Information 15, no. 10 (2024): 631. http://dx.doi.org/10.3390/info15100631.

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The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attacker
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Mohit Angurala. "Enhancing Cybersecurity in Wireless Sensor Networks: AI Solutions to Simulated Attacks." Journal of Information Systems Engineering and Management 10, no. 3s (2025): 217–29. https://doi.org/10.52783/jisem.v10i3s.373.

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Owing to the real time applications of Wireless Sensor Networks (WSNs) including: industrial automation and remote environment monitoring, WSNs have revolutionized today’s infrastructure. While implementing WSNs in strategic areas, security threats have become increasingly prevalent. Security enhancement in WSN by adopting advanced techniques in machine learning is the major focus of this research work. In an effort to discover possible use of Random Forest and Isolation Forest algorithms on them to detect and prevent the attacks, we look into depth of the attack. In this paper, the dataset is
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Wei, Min, Kee Wook Rim, and Kee Cheon Kim. "An Intrusion Detection Scheme for Home Wireless Sensor Networks." Applied Mechanics and Materials 121-126 (October 2011): 3799–804. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3799.

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In this paper, we propose an intrusion detection framework through multi-agents scheme for wireless home automation networks. Our mechanisms include the wireless sensor network intrusion detection architecture and an intrusion detection scheme for security enhancement. For the performance evaluation of our mechanism, we use the wireless data measured on the real wireless home networks. The simulation results show that the security manager detect the intrusion attack to improve the whole performance of the system, and can prolong the lifetime of the network.
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Vinay Tila Patil. "Deep Learning-Driven IoT Defence: Comparative Analysis of CNN and LSTM for DDoS Detection and Mitigation." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 08–21. https://doi.org/10.52783/jisem.v10i8s.951.

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The extensive utilization of Internet of Things (IoT) devices has revolutionized multiple sectors, ranging from smart homes to industrial automation, while concurrently broadening the attack surface for cyber threats, including Distributed Denial of Service (DDoS) attacks. This study examines the efficacy of Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) in detecting DDoS attacks, focusing on the distinct security concerns presented by IoT networks. Employing the extensive CICDDoS2019 dataset, these algorithms scrutinize individual IP flow records to attain re
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Kasturi, Santanam, Xiaolong Li, Peng Li, and John Pickard. "A Proposed Approach to Integrate Application Security Vulnerability Data with Incidence Response Systems." American Journal of Networks and Communications 13, no. 1 (2024): 19–29. http://dx.doi.org/10.11648/j.ajnc.20241301.12.

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This paper has proposed a method to develop an attack tree, from application vulnerability data discovered through tests and scans and correlation analysis using incoming transaction requests monitored by a Web Application Firewall (WAF) tool. The attack tree shows multiple pathways for an attack to shape through vulnerability linkages and a deeper analysis of the Common Weakness Enumeration (CWE) and Common Vulnerability Exposure (CVE) mapping to individual vulnerabilities. By further relating to a parent, peer, or child CWE (including CWEs that follow another CWE and in some cases precede ot
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Lu, Kang-Di, Guo-Qiang Zeng, Xizhao Luo, Jian Weng, Weiqi Luo, and Yongdong Wu. "Evolutionary Deep Belief Network for Cyber-Attack Detection in Industrial Automation and Control System." IEEE Transactions on Industrial Informatics 17, no. 11 (2021): 7618–27. http://dx.doi.org/10.1109/tii.2021.3053304.

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Gopi R. "Cyber-Physical System Defense Against Structured False Data Injection Attacks Using an Adaptive Security Framework with Passivity Enhancement." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 234–44. https://doi.org/10.52783/jisem.v10i43s.8360.

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System integrity, operation, and significant breakdowns can be compromised by coordinated False Data Injection Attacks (FDIAs), which are increasingly prevalent in Cyber-Physical Systems (CPS). Because they are dynamic and constantly evolving, these threats often bypass traditional security controls. The prompt identification of complex FDIAs, the reduction of anomaly detection false positives, and the maintenance of system stability in hostile environments are some important problems tackled. The Passivity-Enhanced Adaptive Security Framework (PEASF) is introduced in this work as a mechanism
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Khan, Zulfiqar Ali, and Akbar Siami Namin. "A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology." Electronics 11, no. 23 (2022): 3892. http://dx.doi.org/10.3390/electronics11233892.

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The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack.
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Guo, Hui Ling. "Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection." Advanced Materials Research 760-762 (September 2013): 857–61. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.857.

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It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.
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Meleshko, Alexey, Anton Shulepov, Vasily Desnitsky, Evgenia Novikova, and Igor Kotenko. "Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems." Water 14, no. 15 (2022): 2342. http://dx.doi.org/10.3390/w14152342.

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The specificity of the water treatment field, associated with water transmission, distribution and accounting, as well as the need to use automation and intelligent tools for various information solutions and security tools, have resulted in the development of integrated approaches and practical solutions regarding various aspects of the functioning of such systems. The research problem lies in the insecurity of water treatment systems and their susceptibility to malicious influences from the side of potential intruders trying to compromise the functioning. To obtain initial data needed for as
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Sun, Zhong, and Xinchun Jie. "Research on Attack Node Localization in Cyber–Physical Systems Based on Residual Analysis and Cooperative Game Theory." Electronics 14, no. 15 (2025): 2943. https://doi.org/10.3390/electronics14152943.

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With the widespread application of cyber–physical systems (CPS) in the field of automation, security concerns have become increasingly prominent. One critical and urgent challenge is the accurate identification of sensor nodes compromised by false data injection (FDI) attacks in multiple-input multiple-output (MIMO) control systems. Building on the implementation of multi-step sampling and residual-based anomaly detection using a support vector machine (SVM), this paper further introduces the Shapley value evaluation method from cooperative game theory and a voting mechanism, and proposes a me
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Sayali Renuse. "Enhancing IoT Security with Activity-Based Attack Modeling and Hybrid Classification Techniques." Panamerican Mathematical Journal 34, no. 1 (2024): 1–13. http://dx.doi.org/10.52783/pmj.v34.i1.900.

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The proliferation of Internet of Things (IoT) devices in industrial environments (Industrial IoT or IIoT) has brought about significant advancements in automation and data analytics. However, the integration of these devices also introduces new security vulnerabilities, making them prime targets for cyber-attacks. This study aims to enhance the security of IIoT systems by employing an activity-based attack modeling approach coupled with hybrid classification techniques. Our proposed method leverages a hybrid GRU-LSTM model to detect and mitigate security threats in real-time. Activity-based at
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Aakarsh Mavi. "Bridging the Gap: Cybersecurity Automation for Legacy Manufacturing Systems." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 21–33. https://doi.org/10.52783/jisem.v10i30s.4768.

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Legacy manufacturing systems play a big role in industrial production, but they usually don’t have strong cyber- security measures in place, which makes them easy targets for modern cyber threats. Because these systems are often outdated, they present serious security risks, as they weren’t built to defend against today’s cyber-attacks. This study aims to fill the cybersecurity gap in legacy manufacturing environments by creating automated tools that boost the security of these systems without needing a lot of hands-on work. The frame- work includes automated patch management and vulnerability
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Barletta, Vita Santa, Danilo Caivano, Mirko De Vincentiis, Azzurra Ragone, Michele Scalera, and Manuel Ángel Serrano Martín. "V-SOC4AS: A Vehicle-SOC for Improving Automotive Security." Algorithms 16, no. 2 (2023): 112. http://dx.doi.org/10.3390/a16020112.

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Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of
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40

Han-Mei Liu. "AI-Enabled Adaptive Cybersecurity Response Using Reinforcement Learning." Frontiers in Artificial Intelligence Research 2, no. 1 (2025): 1–12. https://doi.org/10.71465/gwa30h81.

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Cyber threats are evolving in complexity and frequency, rendering traditional cybersecurity response mechanisms insufficient. Conventional rule-based and supervised machine learning (ML) models struggle to adapt to novel attack patterns, leaving security systems vulnerable to emerging threats. Reinforcement learning (RL) offers a promising approach to adaptive cybersecurity by enabling systems to learn optimal defense strategies through continuous interaction with adversarial environments. This study explores an RL-based cybersecurity response framework that dynamically adjusts mitigation stra
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T, Sowmika, Rohith Paul L, and Malathi G. "IOT Based Smart Rodent Detection and Fire Alert System in Farmland." International Research Journal of Multidisciplinary Technovation 2, no. 3 (2020): 1–6. http://dx.doi.org/10.34256/irjmt2031.

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Agriculture is playing an important role in the development of a country. In this work, a smart and safe agriculture system is proposed that would notify the farmer about the deficit of moisture in the soil, snakes concealed under the soil, passers crossing the farm at odd times using IOT. The flame sensor and humidity sensor are deployed in farms in order to find humidity and detect fire attack. It is virtually hard for everyone to monitor the growth of plants in a large agricultural farmland. The approach that can be used to solve this problem is using IOT based sensor networks to assist the
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B, Vivekanadam. "IoT based Smart Grid Attack Resistance in AC and DC State Estimation." Journal of Electrical Engineering and Automation 2, no. 3 (2021): 118–22. http://dx.doi.org/10.36548/jeea.2020.3.002.

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Use of automation and intelligence in smart grids has led to implementation in a number of applications. When internet of things is incorporated it will result in the significant improvement a number of factors such as fault recovery, energy delivery efficiency, demand response and reliability. However, the collaboration of internet of things and smart grid gives rise to a number of security issues and threats. This is especially the case when using internet based protocols and public communication infrastructure. To address these issues we should ensure that the data stored is secure and crit
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Rao, Alwal Keerthan, and T. Rajashekar Reddy. "AUTONOMOUS MISSILE DEFENSE SYSTEM: INTEGRATING ADVANCED SONAR-BASED TRACKING FOR PRECISE DETECTION." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 14, no. 2 (2023): 1055–60. http://dx.doi.org/10.61841/turcomat.v14i2.14332.

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The objective of this project is to develop and build an automated system for detecting and neutralizing missiles. This system is specifically engineered to identify and track the target (missile) while it maneuvers in various directions. The automated target destruction system tracks the missile's trajectory and engages it by precisely aligning and firing onto the target. This system comprises an advanced sonar-based object tracking system that continuously monitors the target. Once the target is detected, it transmits the precise location of the target to a Central Control System. The Centra
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Ilokanuno, Ogochukwu A. "Smart Meter Tampering Detection Using IoT Based Unsupervised Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5434–45. http://dx.doi.org/10.22214/ijraset.2024.61153.

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Abstract: This work presents a novel smart grid tampering detection system re-engineered for end user monitoring and pipeline automation. The research focused on distributed energy resources. In context, the end user load profile, and generation capacity were processed in the cloud environment for tampering management. Computational pipelined methodology was adopted using baseline data from an independent electricity consumption data from 2018-2021 Abuja. First, a smart grid (SG) survey was carried using existing home estate at Abuja to ascertain tampering procedures in distributed energy reso
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Toliupa, Serhii, and Maksym Kotov. "PROTECTION MODEL AGAINST DISTRIBUTED GRADUAL DEGRADATION ATTACKS BASED ON STATISTICAL AND SEMANTIC APPROACHES." Information systems and technologies security, no. 2 (8) (2024): 26–33. https://doi.org/10.17721/ists.2024.8.26-33.

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B a c k g r o u n d . Nowadays, every critical sector of social institutions conducts its operations on top of distributed processing systems. Contemporary digital infrastructure heavily relies on user-provided data in its operation. As a result, distributed attacks based on botnets are in a continuous state of arms race with the protection methods that filtrate malicious data influx. A common method to do so often relies on heuristics and human-oriented verifications. As the new advancements in the field of artificial intelligence emerge, such attacks adopt new oblique paths towards achieving
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Feng, Tao, and Bugang Zhang. "Security Evaluation and Improvement of the Extended Protocol EIBsec for KNX/EIB." Information 14, no. 12 (2023): 653. http://dx.doi.org/10.3390/info14120653.

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The European Installation Bus(EIB) protocol, also known as KNX/EIB, is widely used in building and home automation. An extension of the KNX/EIB protocol, EIBsec, is primarily designed to meet the requirements for data transmission security in distributed building automation systems. However, this protocol has some security issues in the request, key distribution, and identity authentication processes. This paper employs a formal analysis method that combines Colored Petri Net (CPN) theory with the Dolev-Yao attack model to evaluate and enhance the EIBsec protocol. It utilizes the CPN Tools to
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Dinkar, Amit Kumar, and Ajay Kumar Choudhary. "Exploring Intrusion Detection Systems (IDS) in IoT Environments." Seminars in Medical Writing and Education 3 (December 30, 2024): 552. https://doi.org/10.56294/mw2024552.

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Introduction; The Internet of Things (IoT) has revolutionized numerous sectors, such as home automation, healthcare, and industrial operations, by enabling interconnected devices to facilitate automation, real-time data analysis, and intelligent decision-making. Despite its transformative potential, the rapid proliferation of IoT has introduced critical cybersecurity challenges due to the heterogeneous and fragmented nature of IoT environments. Objective; IoT networks consist of diverse devices with varying capabilities and protocols, making the implementation of standardized security measures
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Daniel, Nir, Florian Klaus Kaiser, Shay Giladi, et al. "Labeling Network Intrusion Detection System (NIDS) Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models." Big Data and Cognitive Computing 9, no. 2 (2025): 23. https://doi.org/10.3390/bdcc9020023.

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Analysts in Security Operations Centers (SOCs) are often occupied with time-consuming investigations of alerts from Network Intrusion Detection Systems (NIDSs). Many NIDS rules lack clear explanations and associations with attack techniques, complicating the alert triage and the generation of attack hypotheses. Large Language Models (LLMs) may be a promising technology to reduce the alert explainability gap by associating rules with attack techniques. In this paper, we investigate the ability of three prominent LLMs (ChatGPT, Claude, and Gemini) to reason about NIDS rules while labeling them w
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AlNusif, Mohammed. "Emerging Threats in Cybersecurity: A Comprehensive Analysis of DDoS and Social Engineering Attacks." International Journal of Engineering and Computer Science 14, no. 07 (2025): 27473–87. https://doi.org/10.18535/ijecs.v14i07.5185.

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In the rapidly evolving landscape of cybersecurity, organizations are increasingly vulnerable to two prominent forms of attacks: Distributed Denial of Service (DDoS) and Social Engineering. These attack vectors, while distinct in execution, share a common goal—disrupting the confidentiality, integrity, or availability of systems and data. This paper provides an in-depth exploration of both threats by examining their methodologies, real-world applications, and the socio-technical implications they present in digital infrastructure. Social Engineering exploits the psychological tendencies of ind
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Lee, Juyoung, Yeonsu Jeong, Taehyun Han, and Taejin Lee. "LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis." Applied Sciences 15, no. 13 (2025): 7237. https://doi.org/10.3390/app15137237.

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As cyber threats become increasingly sophisticated, existing log-based anomaly detection models face critical limitations in adaptability, semantic interpretation, and operational automation. Traditional approaches based on CNNs, RNNs, and LSTMs struggle with inconsistent log formats and often lack interpretability. To address these challenges, we propose LogRESP-Agent, a modular AI framework built around a reasoning-based agent for log-driven security prediction and response. The architecture integrates three core capabilities, including (1) LLM-based anomaly detection with semantic explanati
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