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Wu, Zhijun, Yue Yin, Guang Li, and Meng Yue. "Coherent Detection of Synchronous Low-Rate DoS Attacks." Security and Communication Networks 2021 (March 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/6694264.
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Low-rate denial-of-service (LDoS) attacks are characterized by low average rate and periodicity. Under certain conditions, the high concealment of LDoS attacks enables them to transfer the attack stream to the network without being detected at all before the end. In this article, plenty of LDoS attack traffic is spread to the victim end to detect LDoS attacks. Through experimental analysis, it is found that the attack pulses at the victim end have sequence correlation, so the coherence detection technology in spread spectrum communication is proposed to detect LDoS attacks. Therefore, this paper proposes an attack detection method based on coherent detection, which adopts bivariate cyclic convolution algorithm. Similar to the generation of receiving terminal phase dry detection code in spread spectrum communication, we construct a local detection sequence to complete the extraction of LDoS attack stream from the background traffic of the victim terminal, that is, the coherent detection of LDoS attacks. When predicting the features of an LDoS attack, how to construct the parameters of the detection sequence (such as period, pulse duration, amplitude, and so on) is very important. In this paper, we observe the correlation of LDoS attacks and use coherence detection to detect LDoS attacks. By comparing calculated cross-correlation values with designed double threshold rules, the existence of attacks can be determined. The simulation platform and experiments show that this method has high detection performance.
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Xia, Kui Liang. "Modeling and Simulation of Low Rate of Denial of Service Attacks." Applied Mechanics and Materials 484-485 (January 2014): 1063–66. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.1063.
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The low-rate denial of service attack is more applicable to the network in recent years as a means of attack, which is different from the traditional field type DoS attacks at the network end system or network using adaptive mechanisms exist loopholes flow through the low-rate periodic attacks on the implementation of high-efficiency attacked by an intruder and not be found, resulting in loss of user data or a computer deadlock. LDos attack since there has been extensive attention of researchers, the attack signature analysis and detection methods to prevent network security have become an important research topic. Some have been proposed for the current attacks were classified LDoS describe and model, and then in NS-2 platform for experimental verification, and then LDoS attack detection to prevent difficulties are discussed and summarized for the future such attacks detection method research work to provide a reference.
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Aladaileh, Mohammad Adnan, Mohammed Anbar, Ahmed J. Hintaw, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Taief Alaa Al-Amiedy, and Dyala R. Ibrahim. "Effectiveness of an Entropy-Based Approach for Detecting Low- and High-Rate DDoS Attacks against the SDN Controller: Experimental Analysis." Applied Sciences 13, no. 2 (January 5, 2023): 775. http://dx.doi.org/10.3390/app13020775.
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Software-defined networking (SDN) is a unique network architecture isolating the network control plane from the data plane, offering programmable elastic features that allow network operators to monitor their networks and efficiently manage them. However, the new technology is security deficient. A DDoS attack is one of the common attacks that threaten SDN controllers, leading to the degradation or even collapse of the entire SDN network. Entropy-based approaches and their variants are considered the most efficient approaches to detecting DDoS attacks on SDN controllers. Therefore, this work analyzes the feasibility and impacts of an entropy-based DDoS attack detection approach for detecting low-rate and high-rate DDoS attacks against the controller, measured in terms of detection rate (DR) and false-positive rate (FPR), triggered by a single or multiple host attacks targeting a single or multiple victims. Eight simulation scenarios, representing low and high DDoS attack traffic rates on the controller, have been used to evaluate an entropy-based DDoS attack detection approach. The experimental results reveal that the entropy-based approach enhances the average DR for detecting high-rate DDoS attack traffic compared with low-rate DDoS attack traffic by 6.25%, 20.26%, 6.74%, and 8.81%. In addition, it reduces the average FPRs for detecting a high DDoS attack traffic rate compared with a low DDoS attack traffic rate by 67.68%, 77.54%, 66.94%, and 64.81.
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Ash, Caroline. "Attack rate in Manaus." Science 371, no. 6526 (January 14, 2021): 248.16–250. http://dx.doi.org/10.1126/science.371.6526.248-p.
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Miao, Yuantian, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, and Yang Xiang. "Machine Learning–based Cyber Attacks Targeting on Controlled Information." ACM Computing Surveys 54, no. 7 (July 2021): 1–36. http://dx.doi.org/10.1145/3465171.
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Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects—detection, disruption, and isolation.
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Kruszewski, Piotr, Carlos Bordini, Alf O. Brubakk, and Ottar Sjaastad. "Cluster Headache: Alterations in Heart Rate, Blood Pressure and Orthostatic Responses During Spontaneous Attacks." Cephalalgia 12, no. 3 (June 1992): 172–77. http://dx.doi.org/10.1046/j.1468-2982.1992.1203172.x.
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Changes in heart rate and blood pressure (BP) have been monitored beat-to-beat in a cluster headache patient with and without attacks using a non-invasive Doppler servo method. Two attacks were monitored and during one of them a tilt test was carried out. The variability of heart rate and BP was greater during the attack than during the interparoxysmal period. A marked bradycardia occurred during attacks. Systolic BP increased slightly. There was no heart rate increase after tilting during the attack, whereas this was present invariably during tests carried out interparoxysmally. BP changes during “attack tilt” were difficult to evaluate because of large variation. This may be the first observation of a baroreflex arc dysfunction during a cluster headache attack.
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Zhou, Lu, Mingchao Liao, Cao Yuan, and Haoyu Zhang. "Low-Rate DDoS Attack Detection Using Expectation of Packet Size." Security and Communication Networks 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3691629.
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Low-rate Distributed Denial-of-Service (low-rate DDoS) attacks are a new challenge to cyberspace, as the attackers send a large amount of attack packets similar to normal traffic, to throttle legitimate flows. In this paper, we propose a measurement—expectation of packet size—that is based on the distribution difference of the packet size to distinguish two typical low-rate DDoS attacks, the constant attack and the pulsing attack, from legitimate traffic. The experimental results, obtained using a series of real datasets with different times and different tolerance factors, are presented to demonstrate the effectiveness of the proposed measurement. In addition, extensive experiments are performed to show that the proposed measurement can detect the low-rate DDoS attacks not only in the short and long terms but also for low packet rates and high packet rates. Furthermore, the false-negative rates and the adjudication distance can be adjusted based on the detection sensitivity requirements.
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Lysenko, Sergii, Kira Bobrovnikova, Serhii Matiukh, Ivan Hurman, and Oleg Savenko. "Detection of the botnets’ low-rate DDoS attacks based on self-similarity." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3651. http://dx.doi.org/10.11591/ijece.v10i4.pp3651-3659.
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An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.
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Lei, Gang, Lejun Ji, Ruiwen Ji, Yuanlong Cao, Xun Shao, and Xin Huang. "Extracting Low-Rate DDoS Attack Characteristics: The Case of Multipath TCP-Based Communication Networks." Wireless Communications and Mobile Computing 2021 (July 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/2264187.
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The multipath TCP (MPTCP) enables multihomed mobile devices to realize multipath parallel transmission, which greatly improves the transmission performance of the mobile communication network. With the rapid development of all kinds of emerging technologies, network attacks have shown a trend of development with many types and rapid updates. Among them, low-rate distributed denial of service (LDDoS) attacks are considered to be one of the most threatening issues in the field of network security. In view of the current research status, by using the network simulation software NS2, this paper first compares and analyzes the throughput and delay performance of the MPTCP transmission system under LDDoS attacks and, further, conducts simulation experiments and analysis on the queue occupancy rate of the LDDoS attack flow to extract the basic attack characteristics of the LDDoS attacks. The experimental results show that the LDDoS attacks will have a major destructive effect on the throughput performance and delay performance of the MPTCP transmission system, resulting in a decrease in the robustness of the transmission system. By analyzing and comparing the occupancy rate of the LDDoS attack flow in the MPTCP transmission system, it can be concluded that (1) the occupancy rate of the LDDoS scattered pulse traffic sent by each puppet machine changes slightly, and (2) the occupancy rate of LDDoS attack data flow is much greater than that of ordinary TCP data flow.
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Amstey, Marvin S. "Attack rate for neonatal herpesvirus." American Journal of Obstetrics and Gynecology 155, no. 1 (July 1986): 229–30. http://dx.doi.org/10.1016/0002-9378(86)90127-4.
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Jiang, Yi, and Dengpan Ye. "Black-Box Adversarial Attacks against Audio Forensics Models." Security and Communication Networks 2022 (January 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/6410478.
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Speech synthesis technology has made great progress in recent years and is widely used in the Internet of things, but it also brings the risk of being abused by criminals. Therefore, a series of researches on audio forensics models have arisen to reduce or eliminate these negative effects. In this paper, we propose a black-box adversarial attack method that only relies on output scores of audio forensics models. To improve the transferability of adversarial attacks, we utilize the ensemble-model method. A defense method is also designed against our proposed attack method under the view of the huge threat of adversarial examples to audio forensics models. Our experimental results on 4 forensics models trained on the LA part of the ASVspoof 2019 dataset show that our attacks can get a 99 % attack success rate on score-only black-box models, which is competitive to the best of white-box attacks, and 60 % attack success rate on decision-only black-box models. Finally, our defense method reduces the attack success rate to 16 % and guarantees 98 % detection accuracy of forensics models.
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Singh, Rupinder, Jatinder Singh, and Ravinder Singh. "Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3548607.
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In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS) that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack). For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.
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Fu, Zhongwang, and Xiaohui Cui. "ELAA: An Ensemble-Learning-Based Adversarial Attack Targeting Image-Classification Model." Entropy 25, no. 2 (January 22, 2023): 215. http://dx.doi.org/10.3390/e25020215.
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The research on image-classification-adversarial attacks is crucial in the realm of artificial intelligence (AI) security. Most of the image-classification-adversarial attack methods are for white-box settings, demanding target model gradients and network architectures, which is less practical when facing real-world cases. However, black-box adversarial attacks immune to the above limitations and reinforcement learning (RL) seem to be a feasible solution to explore an optimized evasion policy. Unfortunately, existing RL-based works perform worse than expected in the attack success rate. In light of these challenges, we propose an ensemble-learning-based adversarial attack (ELAA) targeting image-classification models which aggregate and optimize multiple reinforcement learning (RL) base learners, which further reveals the vulnerabilities of learning-based image-classification models. Experimental results show that the attack success rate for the ensemble model is about 35% higher than for a single model. The attack success rate of ELAA is 15% higher than those of the baseline methods.
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Yan, Yao, and Rui Xu. "DDoS Attacks for Ad Hoc Network Based on Attack Cluster." Advanced Materials Research 546-547 (July 2012): 1371–76. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1371.
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Show the definition of Ad Hoc network attack cluster, and propose a new method of DDoS attacks for Ad Hoc Networks, which can accurately attack the target node, demonstrate no redundant aggressive behavior compared with the traditional attack and reduce the detection rate of aggressive behavior. Use NS2 simulation platform to build Ad Hoc network simulation scenarios with dynamic topology, and simulate DDoS attacks in this environment; The simulation results show that the new DDoS attack method can effectively reduce the communication ability of the Ad Hoc network, and increasing the attack node density will strengthen the attack effect.
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Alashhab, Abdussalam Ahmed, Mohd Soperi Mohd Zahid, Mohamed A. Azim, Muhammad Yunis Daha, Babangida Isyaku, and Shimhaz Ali. "A Survey of Low Rate DDoS Detection Techniques Based on Machine Learning in Software-Defined Networks." Symmetry 14, no. 8 (July 29, 2022): 1563. http://dx.doi.org/10.3390/sym14081563.
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Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.
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Vedula, Vasudha, Palden Lama, Rajendra V. Boppana, and Luis A. Trejo. "On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers." Electronics 10, no. 17 (August 30, 2021): 2105. http://dx.doi.org/10.3390/electronics10172105.
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Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub.
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Karthik, M., and M. Krishnan. "Securing an Internet of Things from Distributed Denial of Service and Mirai Botnet Attacks Using a Novel Hybrid Detection and Mitigation Mechanism." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 113–23. http://dx.doi.org/10.22266/ijies2021.0228.12.
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Internet of Things (IoT) has become more familiar in all applications and industrial fields such as medical, military, transportation, etc. It has some limitations because of the attack model in the transmission or communication channel. Moreover, one of the deadliest attacks is known as a Distributed Denial of Service Attack (DDoS). The Presence of DDoS in network layer cause huge damage in data transmission channel that ends in data loss or collapse. To address this issue the current research focused on an innovative detection and mitigation of Mirai and DDoS attack in IoT environment. Initially, number of IoT devices is arranged with the help of a novel Hybrid Strawberry and African Buffalo Optimization (HSBABO). Consequently, the types of DDoS attacks are launched in the developed IoT network. Moreover, the presence of strawberry and African Buffalo fitness is utilized to detect and specify the attack types. Subsequently a novel MCELIECE encryption with Cloud Shield scheme is developed to prevent the low and high rate DDoS attack in the Internet of Things. Finally, the proposed model attained 94% of attack detection accuracy, 3% of false negative rate and 5.5% of false positive rate.
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Yin, Heng, Hengwei Zhang, Jindong Wang, and Ruiyu Dou. "Boosting Adversarial Attacks on Neural Networks with Better Optimizer." Security and Communication Networks 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/9983309.
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Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam iterative fast gradient method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models.
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Miller, Sarah, Laurence Watkins, and Manjit Matharu. "Long-term follow up of intractable chronic short lasting unilateral neuralgiform headache disorders treated with occipital nerve stimulation." Cephalalgia 38, no. 5 (July 14, 2017): 933–42. http://dx.doi.org/10.1177/0333102417721716.
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Background Occipital nerve stimulation is a potential treatment option for medically intractable short-lasting unilateral neuralgiform headache attacks. We present long-term outcomes in 31 patients with short-lasting unilateral neuralgiform headache attacks treated with occipital nerve stimulation in an uncontrolled open-label prospective study. Methods Thirty-one patients with intractable short-lasting unilateral neuralgiform headache attacks were treated with bilateral occipital nerve stimulation from 2007 to 2015. Data on attack characteristics, quality of life, disability and adverse events were collected. Primary endpoint was change in mean daily attack frequency at final follow-up. Results At a mean follow-up of 44.9 months (range 13–89) there was a 69% improvement in attack frequency with a response rate (defined as at least a 50% improvement in daily attack frequency) of 77%. Attack severity reduced by 4.7 points on the verbal rating scale and attack duration by a mean of 64%. Improvements were seen in headache-related disability and depression. Adverse event rates were favorable, with no electrode migration or erosion reported. Conclusion Occipital nerve stimulation appears to offer a safe and efficacious treatment for refractory short-lasting unilateral neuralgiform headache attacks with significant improvements sustained in the long term. The procedure has a low adverse event rate when conducted in highly specialised units.
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Chen, Zhaohe, Ming Tang, and Jinghai Li. "Inversion Attacks against CNN Models Based on Timing Attack." Security and Communication Networks 2022 (February 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/6285909.
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Model confidentiality attacks on convolutional neural networks (CNN) are becoming more and more common. At present, model reverse attack is an important means of model confidentiality attacks, but all of these attacks require strong attack ability, meanwhile, the success rates of these attacks are low. We study the time leakage of CNN running on the SoC (system on-chip) system and propose a reverse method based on side-channel attack. It uses the SDK tool-profiler to collect the time leakage of different networks of various CNNs. According to the linear relationship between time leakage, calculation, and memory usage parameters, we take the profiling attack to establish a mapping library of time and the different networks. After that, the smallest difference between the measured time of unknown models and the theoretical time in the mapping library is considered to be the real parameters of the unknown models. Finally, we can reverse other layers even the entire model. Based on the experiments, the reverse success rate of common convolutional layers is above 78.5%, and the reverse success rates of different CNNs (such as AlexNet, ConvNet, LeNet, etc.) are all above 67.67%. Moreover, the results show that the success rate of our method is 10% higher than the traditional methods on average. In the adversarial sample attack, the success rate reached 97%.
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Perego, Francesca, Beatrice De Maria, Maria Bova, Angelica Petraroli, Azzurra Marcelli Cesoni, Valeria De Grazia, Lorenza Chiara Zingale, Alberto Porta, Giuseppe Spadaro, and Laura Adelaide Dalla Vecchia. "Analysis of Heart-Rate Variability during Angioedema Attacks in Patients with Hereditary C1-Inhibitor Deficiency." International Journal of Environmental Research and Public Health 18, no. 6 (March 12, 2021): 2900. http://dx.doi.org/10.3390/ijerph18062900.
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C1-inhibitor hereditary angioedema (C1-INH-HAE) is a rare disease characterized by self-limiting edema associated with localized vasodilation due to increased levels of circulating bradykinin. C1-INH-HAE directly influences patients’ everyday lives, as attacks are unpredictable in frequency, severity, and the involved anatomical site. The autonomic nervous system could be involved in remission. The cardiac autonomic profile has not yet been evaluated during the attack or prodromal phases. In this study, a multiday continuous electrocardiogram was obtained in four C1-INH-HAE patients until attack occurrence. Power spectral heart rate variability (HRV) indices were computed over the 4 h preceding the attack and during the first 4 h of the attack in three patients. Increased vagal modulation of the sinus node was detected in the prodromal phase. This finding may reflect localized vasodilation mediated by the release of bradykinin. HRV analysis may furnish early markers of an impending angioedema attack, thereby helping to identify patients at higher risk of attack recurrence. In this perspective, it could assist in the timing, titration, and optimization of prophylactic therapy, and thus improve patients’ quality of life.
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Nazih, Waleed, Yasser Hifny, Wail S. Elkilani, Habib Dhahri, and Tamer Abdelkader. "Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks." Sensors 20, no. 20 (October 17, 2020): 5875. http://dx.doi.org/10.3390/s20205875.
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Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities. Experiments show that the system has a high detection accuracy and low detection time. The detection accuracy was higher for low-rate attacks than that of traditional machine learning.
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Leisser, Christoph, and Oliver Findl. "Rate of strokes 1 year after retinal artery occlusion with analysis of risk groups." European Journal of Ophthalmology 30, no. 2 (February 19, 2019): 360–62. http://dx.doi.org/10.1177/1120672119830925.
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Background: The risk of developing stroke after retinal artery occlusion was reported to be increased. The aim of our study was to assess the rate of strokes/transitory ischemic attacks after retinal artery occlusion in a European population and to identify the risk groups. Methods: All patients, diagnosed with branch or central retinal artery occlusion at our outpatient department since 2014, were asked to participate in this prospective case–control study. At the initial examination, the medical history was documented and 1 year after retinal artery occlusion, patients were called by telephone interview for assessment of the rate of strokes/transitory ischemic attack in the follow-up period. Results: In all, 30 eyes of 30 patients could be included. Among these, six patients had a stroke, one a transitory ischemic attack, and one an amaurosis fugax in the medical history before retinal artery occlusion. In the period 1 year after retinal artery occlusion, one patient had a re-stroke and one patient had a transitory ischemic attack, with amaurosis fugax in the medical history. Rates of strokes/transitory ischemic attack before and after retinal artery occlusion did not show significant differences between branch and central artery occlusion. Conclusion: The number of strokes/transitory ischemic attacks within the first year is relatively low after retinal artery occlusion and patients that already had a previous stroke, transitory ischemic attack, and/or amaurosis fugax before retinal artery occlusion seem to have a higher risk for a cerebrovascular event after retinal artery occlusion.
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Du, Xiaohu, Jie Yu, Zibo Yi, Shasha Li, Jun Ma, Yusong Tan, and Qinbo Wu. "A Hybrid Adversarial Attack for Different Application Scenarios." Applied Sciences 10, no. 10 (May 21, 2020): 3559. http://dx.doi.org/10.3390/app10103559.
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Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with the vulnerability and security of deep learning systems. According to whether the attacker understands the deep learning model structure, the adversarial attack is divided into black-box attack and white-box attack. In this paper, we propose a hybrid adversarial attack for different application scenarios. Firstly, we propose a novel black-box attack method of generating adversarial examples to trick the word-level sentiment classifier, which is based on differential evolution (DE) algorithm to generate semantically and syntactically similar adversarial examples. Compared with existing genetic algorithm based adversarial attacks, our algorithm can achieve a higher attack success rate while maintaining a lower word replacement rate. At the 10% word substitution threshold, we have increased the attack success rate from 58.5% to 63%. Secondly, when we understand the model architecture and parameters, etc., we propose a white-box attack with gradient-based perturbation against the same sentiment classifier. In this attack, we use a Euclidean distance and cosine distance combined metric to find the most semantically and syntactically similar substitution, and we introduce the coefficient of variation (CV) factor to control the dispersion of the modified words in the adversarial examples. More dispersed modifications can increase human imperceptibility and text readability. Compared with the existing global attack, our attack can increase the attack success rate and make modification positions in generated examples more dispersed. We’ve increased the global search success rate from 75.8% to 85.8%. Finally, we can deal with different application scenarios by using these two attack methods, that is, whether we understand the internal structure and parameters of the model, we can all generate good adversarial examples.
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Ozdayi, Mustafa Safa, Murat Kantarcioglu, and Yulia R. Gel. "Defending against Backdoors in Federated Learning with Robust Learning Rate." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9268–76. http://dx.doi.org/10.1609/aaai.v35i10.17118.
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Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. At a high level, our defense is based on carefully adjusting the aggregation server's learning rate, per dimension and per round, based on the sign information of agents' updates. We first conjecture the necessary steps to carry a successful backdoor attack in FL setting, and then, explicitly formulate the defense based on our conjecture. Through experiments, we provide empirical evidence that supports our conjecture, and we test our defense against backdoor attacks under different settings. We observe that either backdoor is completely eliminated, or its accuracy is significantly reduced. Overall, our experiments suggest that our defense significantly outperforms some of the recently proposed defenses in the literature. We achieve this by having minimal influence over the accuracy of the trained models. In addition, we also provide convergence rate analysis for our proposed scheme.
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Wang, Chao, Yunxiao Sun, Wenting Wang, Hongri Liu, and Bailing Wang. "Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder." Symmetry 15, no. 3 (February 21, 2023): 568. http://dx.doi.org/10.3390/sym15030568.
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To cope with the rising threats posed by network attacks, machine learning-based intrusion detection systems (IDSs) have been intensively researched. However, there are several issues that need to be addressed. It is difficult to deal with unknown attacks that do not appear in the training set, and as a result, poor detection rates are produced for these unknown attacks. Furthermore, IDSs suffer from high false positive rate. As different models learn data characteristics from different perspectives, in this work we propose a hybrid IDS which leverages both random forest (RF) and autoencoder (AE). The hybrid model operates in two steps. In particular, in the first step, we utilize the probability output of the RF classifier to determine whether a sample belongs to attack. The unknown attacks can be identified with the assistance of the probability output. In the second step, an additional AE is coupled to reduce the false positive rate. To simulate an unknown attack in experiments, we explicitly remove some samples belonging to one attack class from the training set. Compared with various baselines, our suggested technique demonstrates a high detection rate. Furthermore, the additional AE detection module decreases the false positive rate.
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Zhang, Jian, Qidi Liang, Rui Jiang, and Xi Li. "A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors." Applied Sciences 9, no. 21 (October 31, 2019): 4633. http://dx.doi.org/10.3390/app9214633.
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In recent years, distributed denial of service (DDoS) attacks have increasingly shown the trend of multiattack vector composites, which has significantly improved the concealment and success rate of DDoS attacks. Therefore, improving the ubiquitous detection capability of DDoS attacks and accurately and quickly identifying DDoS attack traffic play an important role in later attack mitigation. This paper proposes a method to efficiently detect and identify multivector DDoS attacks. The detection algorithm is applicable to known and unknown DDoS attacks.
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Chen, Xiaoming, Lei Chen, and Yalong Yan. "Detecting a Photon-Number Splitting Attack in Decoy-State Measurement-Device-Independent Quantum Key Distribution via Statistical Hypothesis Testing." Entropy 24, no. 9 (September 2, 2022): 1232. http://dx.doi.org/10.3390/e24091232.
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Measurement-device-independent quantum key distribution (MDI-QKD) is innately immune to all detection-side attacks. Due to the limitations of technology, most MDI-QKD protocols use weak coherent photon sources (WCPs), which may suffer from a photon-number splitting (PNS) attack from eavesdroppers. Therefore, the existing MDI-QKD protocols also need the decoy-state method, which can resist PNS attacks very well. However, the existing decoy-state methods do not attend to the existence of PNS attacks, and the secure keys are only generated by single-photon components. In fact, multiphoton pulses can also form secure keys if we can confirm that there is no PNS attack. For simplicity, we only analyze the weaker version of a PNS attack in which a legitimate user’s pulse count rate changes significantly after the attack. In this paper, under the null hypothesis of no PNS attack, we first determine whether there is an attack or not by retrieving the missing information of the existing decoy-state MDI-QKD protocols via statistical hypothesis testing, extract a normal distribution statistic, and provide a detection method and the corresponding Type I error probability. If the result is judged to be an attack, we use the existing decoy-state method to estimate the secure key rate. Otherwise, all pulses with the same basis leading to successful Bell state measurement (BSM) events including both single-photon pulses and multiphoton pulses can be used to generate secure keys, and we give the formula of the secure key rate in this case. Finally, based on actual experimental data from other literature, the associated experimental results (e.g., the significance level is 5%) show the correctness of our method.
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Li, Dong-Dong, Yan-Lin Tang, Yu-Kang Zhao, Lei Zhou, Yong Zhao, and Shi-Biao Tang. "Security of Optical Beam Splitter in Quantum Key Distribution." Photonics 9, no. 8 (July 28, 2022): 527. http://dx.doi.org/10.3390/photonics9080527.
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The optical beam splitter is an essential device used for decoding in quantum key distribution. The impact of optical beam splitters on the security of quantum key distribution was studied, and it was found that the realistic device characteristics closely influence the error rate introduced by the wavelength-dependent attack on optical beam splitters. A countermeasure, combining device selection and error rate over-threshold alarms, is proposed to protect against such attacks. Beam splitters made of mirror coatings are recommended, and the variation of splitting ratio should be restricted to lower than 1 dB at 1260–1700 nm. For the partial attack scenario where the eavesdropper attacks only a portion of the quantum signal, a modified secure key rate formula is proposed to eliminate the revealed information of the attacked portion. Numerical results show that the QKD system adopting this countermeasure exhibits good performance with a secure key rate of over 10 kbps at 100 km and a maximum transmission distance of over 150 km, with only a small difference from the no-attack scenario. Additionally, a countermeasure to monitor the light intensity of different wavelengths is proposed to protect against the wavelength-dependent attack on optical beam splitters.
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You, Haotian, Yufang Lu, and Haihua Tang. "Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM." Sustainability 15, no. 2 (January 9, 2023): 1233. http://dx.doi.org/10.3390/su15021233.
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Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM.
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Michel, P., B. Dubroca, JF Dartigues, A. El Hasnaoui, and Henry. "Frequency of Severe Attacks in Migraine Sufferers of the Gazel Cohort." Cephalalgia 17, no. 8 (December 1997): 863–66. http://dx.doi.org/10.1046/j.1468-2982.1997.1708863.x.
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The concept of severe migraine was raised to define migraineurs most in need of care and for use in clinical practice. We aimed to measure the frequency of severe attacks in a working sample of 276 migraine sufferers using a diary over a 3-month period. Migraine sufferers recorded each attack's clinical features, the degree of their disability, their use of drugs and the effectiveness of the drugs. Since the definition of severe attack is not standardized, we studied the impact of different definitions on the frequency. The frequency of severe attacks was 0.9% and appeared to be very sensitive to the definitions, ranging between 0.4 and 13%. In France, the extrapolated number of severe attacks is nearly one million out of a total of 115 million. In the migraineurs who had had at least one severe attack, the individual variability of intensity, duration or disability was very high, so the proportion of severe attacks in a given sufferer was low—between 15% and 50%. We conclude that the global concept of severe migraine is not relevant and should be split into two componentssevere attack and severe migraine sufferer. The goals are different, too. Regarding treatment, for example, the severe attack concept is more valid for acute treatment strategies, whereas the severe migraine sufferer concept should be preferred to determine the need for prophylactic treatment. Since much work is being done nowadays to define a rate treatment strategies, definition of the criteria of severe attack and validation of a measurement tool should be a priority.
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Dayananda, Prakyath, Mallikarjunaswamy Srikantaswamy, Sharmila Nagaraju, Rekha Velluri, and Doddananjedevaru Mahesh Kumar. "Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 4 (December 1, 2022): 2086. http://dx.doi.org/10.11591/ijpeds.v13.i4.pp2086-2097.
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The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reconfigurable Euclidean detector (RED) evaluator. The MATLAB function randn() has been used to produce sequence distribution channel noise with mean value zero to analysed the amplitude variation with respect to evolution state variable. The detector noise rate is analysed with respect to threshold. The detection rate of various attacks such as DDOS, Random and false data injection attacks is also analysed. The proposed mathematical model is effectively reconstructed to frame the original sinusoidal signal from the evaluator state variable using reconfigurable Euclidean detectors.
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Clement Sunder, A. John, and A. Shanmugam. "Black Hole Attack Detection in Healthcare Wireless Sensor Networks Using Independent Component Analysis Machine Learning Technique." Current Signal Transduction Therapy 15, no. 1 (July 31, 2020): 56–64. http://dx.doi.org/10.2174/1574362413666180705123733.
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Background: Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed. Materials and Methods: The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors. Results: The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function. Conclusion: The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.
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Repka, Marek, Michal Varchola, and Miloš Drutarovský. "Improving CPA Attack Against DSA and ECDSA." Journal of Electrical Engineering 66, no. 3 (May 1, 2015): 159–63. http://dx.doi.org/10.2478/jee-2015-0025.
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Abstract In this work, we improved Correlation Power Analysis (CPA) attack against Digital Signature Algorithm (DSA) and its various derivations, such as Elliptic Curve Digital Signature Algorithm (ECDSA). The attack is aimed against integer multiplication with constant secret operand. We demonstrate this improvement on 16-bit integer multiplier in FPGA. The improvement makes it possible to guess more blocks of key, and the improvement also eliminates errors of simulated attacks what is very important when approximating attack success rate and complexity based on simulated attacks. We also discus a possible efficient countermeasure.
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Siallagan, Ricardo, Widya Lestari, and Dini Hariyati Adam. "The Attack Rate of Pocket Caterpillar (Metisa plana) And How To Control It on Oil Palm (Elaeis Guineensis Jacq) Plantation At PT Umada Pernantian “A”, North of Labuhanbatu Regency." JURNAL PEMBELAJARAN DAN BIOLOGI NUKLEUS 8, no. 2 (July 25, 2022): 493–503. http://dx.doi.org/10.36987/jpbn.v8i2.2686.
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Oil palm (Elaeis guineensis jacq.), is a vegetable oil-producing plant, which cannot be separated from the attack of caterpillars that eat oil palm leaves which can cause a decrease in plant production. The purpose of this study was to determine the attack rate of bagworm (Metisa plana) and how to control it, on each oil palm plant block at PT. Umada Pernantian "A", Kec. Marbau, North Labuhan Batu Regency. The method used in this research is descriptive method by collecting primary data and secondary data. Primary data is the result of observations in the field with a pest census using a descriptive method. Secondary data is data from the previous pest census in the company. The highest attack rate of Metisa plana was found in block I and block II with an incidence of 18% in block I and 8% in block II, the number of pests in block I was 27,336, and 18,480 in block II, which was >5 tails / midrib with weight category. The lowest Metisa plana attack rate was found in block III and block V where the percentage of bagworm attacks in block III: 0.08% and in block V: 0.11%, the number of pests in block III: 23 tails and block V: 26 tails, with an attack rate of <2 bagworms per midrib. After chemical control was carried out in blocks I and II, the percentage of attacks in block I: 0.43% and block II: 0.34%, the number of pests in block I: 100 and 70 in block II, the attack rate in block I and II were being <2 tails/midrib in the light category
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Aineyoona, Patrick. "A MACHINE LEARNING ALGORITHM WITH SELF-UPDATE PARAMETER CALIBRATION TO IMPROVE INTRUSION DETECTION OF DDOS IN COMMUNICATION NETWORKS." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (October 1, 2021): 72–79. http://dx.doi.org/10.33564/ijeast.2021.v06i06.008.
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Currently DDoS attack has become one of the most common network attacks worldwide. This is largely due to the fact that we live in the age of the Internet of Things, with the rapid development of computer and communication technology evolving into big, complex and distributed systems that are exposed to several kinds of attacks in addition to new threats. In order to detect intruders in an efficient and timely manner, a real time detection mechanism, proficient in dealing with a variety of forms of attacks is highly important. However, due to the uniformity and evolution of DDoS attack modes and the variable size of attack traffic, there has not yet been a detection method with satisfactory detection accuracy at present and considerable effort made by both the scientific research and industry for several years to mitigate DDoS detection potential DDoS target indicate that DDoS attacks have not been fully addressed. This study therefore aimed at developing a machine learning a Machine learning algorithm with self-update parameter calibration to improve intrusion detection of DDoS in communication networks, in two steps: Feature extraction and model detection that is, we extract DDoS attack traffic characteristics with large proportion and compare the data packages according to the protocol in the Feature extraction stage whereas in the model detection stage, the features that were extracted are used as the input features in machine learning after which the Random Forest algorithm used to train the developed detection model. Finally, the model was validated by three metrics (accuracy, false negative rate and false positive rate). The results show that the DDoS attack detection method based on machine learning proposed in this study has a good detection rate and accuracy compared to the current popular DDoS attack detection methods. The developed model achieved accuracy of 96% over a real-time dataset.
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Patel, Niranjan Singh, and Tryambak Hiwarkar. "Comparative Analysis of Several Anomaly Detection Algorithm with its Impact Towards the Security and its Performance." International Journal of Computer Science and Mobile Computing 11, no. 6 (June 30, 2022): 78–86. http://dx.doi.org/10.47760/ijcsmc.2022.v11i06.006.
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Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machine learning models are introduced for intelligent detection of abnormal traffic data. A Denial of Service (DoS) attack is a malicious effort to keep endorsed users of a website or web service from accessing it, or limiting their ability to do so. A Distributed Denial of Service (DDoS) attack is a type of DoS attack in which many computers are used to cripple a web page, website or web based service. Fault either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched of the type of network attacks, denial-of-service flood attacks have reason the most severe impact. This analysis study on flood attacks and Flash Crowd their improvement, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are appraised against principle related to their characteristics, technique and collision. This paper discusses a statistical approach to analysis the distribution of network traffic to recognize the normal network traffic behavior. This paper also discusses a various method to recognize anomalies in network traffic.
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Kareem, Morenikeji Kabirat, Olaniyi Dada Aborisade, Saidat Adebukola Onashoga, Tole Sutikno, and Olaniyi Mathew Olayiwola. "Efficient model for detecting application layer distributed denial of service attacks." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 441–50. http://dx.doi.org/10.11591/eei.v12i1.3871.
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The increasing advancement of technologies and communication infrastructures has been posing threats to the internet services. One of the most powerful attack weapons for disrupting web-based services is the distributed denial of service (DDoS) attack. The sophisticated nature of attack tools being created and used for launching attacks on target systems makes it difficult to distinguish between normal and attack traffic. Consequently, there is a need to detect application layer DDoS attacks from network traffic efficiently. This paper proposes a detection system coined eXtreme gradient boosting (XGB-DDoS) using a tree-based ensemble model known as XGBoost to detect application layer DDoS attacks. The Canadian institute for cybersecurity intrusion detection systems (CIC IDS) 2017 dataset consisting of both benign and malicious attacks was used in training and testing of the proposed model. The performance results of the proposed model indicate that the accuracy rate, recall, precision rate, and F1-score of XGB-DDoS are 0.999, 0.997, 0.995, and 0.996, respectively, as against those of k-nearest neighbor (KNN), support vector machine (SVM), principal component analysis (PCA) hybridized with XGBoost, and KNN with SVM. So, the XGB-DDoS detection model did better than the models that were chosen. This shows that it is good at finding application layer DDoS attacks.
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Desai, Vinod, and Dinesha Hagare Annappaiah. "Reputation-based Security model for detecting biased attacks in BigData." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1567. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1567-1576.
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As internet of things (IoT) devices are increasing since the emergence of these devices in 2010, the data stored by these devices should have a proper security measure so that it can be stored without getting in hands of an attacker. The data stored has to be analyzed whether the data is safe or malicious, as the malicious data can corrupt the whole information. The security model in BigData has many challenges such as vulnerability to fake data generation, troubles with cryptographic protection, and absent security audits. As cyberattacks are increasing the main objective of each organization is to secure the data efficiently. This paper presents a model of reputation security for the detection of biased attacks on BigData. The proposed model provides various evaluation models to identify biased attack in malicious IoT devices and provide a secure communication metric for BigData. The results show better rates in terms of attack detection rate, attack detection failure rata, system throughput and number of dead nodes when the attack rate is increased when compared with the existing reputation-based security (ERS) model. Moreover, this model reputation-based biased attack detection (RBAD) increases the security of the IoT devices in the BigData and reduces the biased attack coming from various malicious nodes.
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P. Narode, Miss Priyanka, and Prof I. R. Shaikh. "Review on EM-CURE Algorithm for Detection DDOS Attack." International Journal Of Engineering And Computer Science 7, no. 01 (January 10, 2018): 23386–489. http://dx.doi.org/10.18535/ijecs/v7i1.04.
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Distributed Denial of Service attack (DoS attack) is a cyber attack where the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the internet. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method because they are designed to restricted applications on limited environments.DDoS attack detection very difficult because the non-existence of predefined rules to correctly identify the genuine network flow. A combination of unsupervised data mining techniques as IDS are introduced. The Entropy Method concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in network flow. The data is mainly collected from datasets. The CURE DDoS attack detection technique based on entropy gives a promising way to analyze this attack and construct an efficient detection model using a clustering data mining techniques. This approach has been evaluated and compared with several existing approaches in terms of accuracy, false alarm rate, detection rate, F. measure and Phi coefficient.
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Aladaileh, Mohammad A., Mohammed Anbar, Iznan H. Hasbullah, and Yousef K. Sanjalawe. "Information Theory-based Approaches to Detect DDoS Attacks on Software-defined Networking Controller a Review." International Journal of Education and Information Technologies 15 (April 22, 2021): 83–94. http://dx.doi.org/10.46300/9109.2021.15.9.
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The number of network users and devices has exponentially increased in the last few decades, giving rise to sophisticated security threats while processing users’ and devices’ network data. Software-Defined Networking (SDN) introduces many new features, but none is more revolutionary than separating the control plane from the data plane. The separation helps DDoS attack detection mechanisms by introducing novel features and functionalities. Since the controller is the most critical part of the SDN network, its ability to control and monitor network traffic flow behavior ensures the network functions properly and smoothly. However, the controller’s importance to the SDN network makes it an attractive target for attackers. Distributed Denial of Service (DDoS) attack is one of the major threats to network security. This paper presents a comprehensive review of information theory-based approaches to detect low-rate and high-rate DDoS attacks on SDN controllers. Additionally, this paper provides a qualitative comparison between this work and the existing reviews on DDoS attack detection approaches using various metrics to highlight this work’s uniqueness. Moreover, this paper provides in-depth discussion and insight into the existing DDoS attack detection approaches to point out their weaknesses that open the avenue for future research directions. Meanwhile, the finding of this paper can be used by other researchers to propose a new or enhanced approach to protect SDN controllers from the threats of DDoS attacks by accurately detecting both low-rate and high-rate DDoS attacks.
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Djanie, Tutu, and Dzisi. "A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques." Computers 8, no. 4 (November 26, 2019): 85. http://dx.doi.org/10.3390/computers8040085.
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A denial of service (DoS) attack in a computer network is an attack on the availability of computer resources to prevent users from having access to those resources over the network. Denial of service attacks can be costly, capable of reaching $100,000 per hour. Development of easily-accessible, simple DoS tools has increased the frequency and reduced the level of expertise needed to launch an attack. Though these attack tools have been available for years, there has been no proposed defense mechanism targeted specifically at them. Most defense mechanisms in literature are designed to defend attacks captured in datasets like the KDD Cup 99 dataset from 20 years ago and from tools no longer in use in modern attacks. In this paper, we capture and analyze traffic generated by some of these DoS attack tools using Wireshark Network Analyzer and propose a signature-based DoS detection mechanism based on SVM classifier to defend against attacks launched by these attack tools. Our proposed detection mechanism was tested with Snort IDS and compared with some already existing defense mechanisms in literature and had a high detection accuracy, low positive rate and fast detection time.
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Sun, De Gang, Kun Yang, Xiang Jing, Bin Lv, and Yan Wang. "Abnormal Network Traffic Detection Based on Conditional Event Algebra." Applied Mechanics and Materials 644-650 (September 2014): 1093–99. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1093.
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Network anomaly traffic detection can discover network abnormal behavior and unknown network attacks. But anomaly detection system in current has the disadvantage of the high rate of false positives. Because the condition is not sufficient and high-order conditional reasoning cannot be computed, it leads inaccurate detection of abnormal behavior. In this paper, an analysis method for abnormal network traffic detection is presented. The method firstly applied conditional event algebra for abnormal network traffic detection of Denial-of-Service (DoS) attacks on the 10% trainset of KDD Cup 99 data set. Neptune attack, as an instance of DoS attacks, is used to illustrate this method. Firstly of all, introducing analyzes the attack process of neptune attack. Then, Selecting the most related features of neptune attack on KDD Cup 99 data set and summarizes the basic flow chart of neptune attack. Finally, applying this method for detection of Neptune attack, it can be found that this method can handle with high-order conditional reasoning under insufficient situation, and detect network abnormal behavior.
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Bilen, Abdulkadir, and Ahmet Bedri Özer. "Cyber-attack method and perpetrator prediction using machine learning algorithms." PeerJ Computer Science 7 (April 9, 2021): e475. http://dx.doi.org/10.7717/peerj-cs.475.
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Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime prediction are available in the literature. On the other hand, they suffer from a deficiency in predicting cyber-crime and cyber-attack methods. This problem can be tackled by identifying an attack and the perpetrator of such attack, using actual data. The data include the type of crime, gender of perpetrator, damage and methods of attack. The data can be acquired from the applications of the persons who were exposed to cyber-attacks to the forensic units. In this paper, we analyze cyber-crimes in two different models with machine-learning methods and predict the effect of the defined features on the detection of the cyber-attack method and the perpetrator. We used eight machine-learning methods in our approach and concluded that their accuracy ratios were close. The Support Vector Machine Linear was found out to be the most successful in the cyber-attack method, with an accuracy rate of 95.02%. In the first model, we could predict the types of attacks that the victims were likely to be exposed to with a high accuracy. The Logistic Regression was the leading method in detecting attackers with an accuracy rate of 65.42%. In the second model, we predicted whether the perpetrators could be identified by comparing their characteristics. Our results have revealed that the probability of cyber-attack decreases as the education and income level of victim increases. We believe that cyber-crime units will use the proposed model. It will also facilitate the detection of cyber-attacks and make the fight against these attacks easier and more effective.
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Kaidar, E. K., A. K. Turgambayeva, B. S. Zhussupov, B. S. Serik, and M. K. Zhanaliyeva. "Secondary attack rate of the COVID-19." International journal of health sciences 6, no. 3 (September 29, 2022): 1468–82. http://dx.doi.org/10.53730/ijhs.v6n3.13082.
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Introduction: The first case of COVID-19 infection in Kazakhstan was registered on March 13, 2020. At the beginning the detection methods and the load of the spread of the emerging respiratory pathogen were uncertain. This study aimed to assess the incidence of the secondary attack rate among close contacts of confirmed and probable COVID-19 cases living in the same household in Nur-Sultan, Kazakhstan. Methods: The prospective study included 172 participants: 122 confirmed and 50 uncertain cases of COVID-19 with varying degrees of severity as well as their close contacts identified in Nur-Sultan, Kazakhstan from November 26th, 2020 until February 15th, 2021. All participants were tested with PCR and ELISA assays at the time of inclusion and on days 14-21 of follow-up. Results: The most common symptoms among both cohorts of patients were fever (90%) (p=<0.001), cough (78.0%) (p=0.11), difficulty breathing (63.3%) (p=<0.001), chills (60%) (p=<0.001). The effective reproductive Rt number for confirmed cases was 1.43 [95%CI=1.27-1.59], for probable cases 0.96 [95%CI=0.70-1.22]. The mean serial interval was 4.02 (SD 2.52), for confirmed was 4.43 (SD 2.45), for probable cases was 3.21 (SD 2.48). Conclusion: The infection rates following close contact with COVID-19 confirmed and probable cases were 92% and 98.1%, respectively.
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KUSUHARA, K. "Attack rate of exanthem subitum in Japan." Lancet 340, no. 8817 (August 1992): 482. http://dx.doi.org/10.1016/0140-6736(92)91800-n.
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Heithaus, Michael R. "SHARK ATTACKS ON BOTTLENOSE DOLPHINS (TURSIOPS ADUNCUS) IN SHARK BAY, WESTERN AUSTRALIA: ATTACK RATE, BITE SCAR FREQUENCIES, AND ATTACK SEASONALITY." Marine Mammal Science 17, no. 3 (July 2001): 526–39. http://dx.doi.org/10.1111/j.1748-7692.2001.tb01002.x.
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Wang, Jing Lei. "Research on the Detection Method of the Malicious Attacks on Campus Network." Applied Mechanics and Materials 644-650 (September 2014): 3291–94. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3291.
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The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algorithm is proposed. The plane of support vector machine classification is constructed, to complete the malicious attacks detection of campus network. Experiments show that this approach can improve the accuracy rate of the malicious attack detection, and achieve satisfactory results.
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Ye, Jianbin, Xiaoyuan Liu, Zheng You, Guowei Li, and Bo Liu. "DriNet: Dynamic Backdoor Attack against Automatic Speech Recognization Models." Applied Sciences 12, no. 12 (June 7, 2022): 5786. http://dx.doi.org/10.3390/app12125786.
Full textAbstract:
Automatic speech recognition (ASR) is popular in our daily lives (e.g., via voice assistants or voice input). Once its security attributes are destroyed, it poses as a severe threat to a user’s life and ‘property safety’. Prior research has demonstrated that ASR systems are vulnerable to backdoor attacks. A model embedded with a backdoor behaves normally on clean samples yet misclassifies malicious samples that contain triggers. Existing backdoor attacks have mostly been conducted in the image domain. However, they can not be applied in the audio domain because of poor transferability. This paper proposes a dynamic backdoor attack method against ASR models, named DriNet. Significantly, we designed a dynamic trigger generation network to craft a variety of audio triggers. It is trained jointly with the discriminative model incorporated with an attack success rate on poisoned samples and accuracy on clean samples. We demonstrate that DriNet achieves an attack success rate of 86.4% when infecting only 0.5% of the training set without reducing its accuracy. DriNet can still achieve comparable attack performance to backdoor attacks using static triggers, further enjoying richer attack patterns. We further evaluated DriNet’s resistance to a current state-of-the-art defense mechanism. The anomaly index of DriNet is more than 37.4% smaller than that of BadNets method. The triggers generated by DriNet are hard reverse, keeping DriNet from the detectors.
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Al Maamari, Amal S., and Nadir K.Salih. "Comparison between Black Hole and Flooding Attack in Mobile Ad-hoc Network and their Simulation Study." International Journal of Research Publication and Reviews 03, no. 12 (2022): 241–48. http://dx.doi.org/10.55248/gengpi.2022.31202.
Full textAbstract:
Mobile ad hoc network (MANET) is dynamic in nature and vulnerable for several attacks to be arising in it. Mobile nodes frequently disconnect and join the network; they can arbitrarily moves from one place to another. In present-day wireless communication scenario, Mobile ad hoc network (MANET) plays a very important role, as it consists of many autonomous nodes which communicate together to form a proper communication network. Each node in a network will move in random path, so that nodes direction will change frequently. This paper describes the features, application, flooding attack and black hole attack in the MANET implemented on AODV protocol. The simulation work is carried out in Network Simulator (NS2.34). The performance analysis is done Nodes with 20 to 60 nodes were used in the AODV routing protocol simulation to produce energy-efficient outcomes, with the flooding attacks and the interruption of blackhole attacks. The average delay, routing overhead, packet drop rate and packet delivery rate are calculated. By the simulation it has been evaluated that in flooding attack the routing overhead is more as compared to the black hole attack. A comparative study is also done on these parameters for all three scenarios. Also we simulated the attack in various wireless ad-hoc network scenarios and have tried to find a response system in simulations.