Results 11 to 20 of about 39,421 (253)
Attack Entropy Optimization Algorithm Based on SQAG Model [PDF]
Jisuanji gongcheng, 2020 In order to reduce network security risks and better realize the optimization of network attack paths,this paper constructs a SQAG model for network attacks based on the existing network attack graphs.The model discretizes the attack process,in which the
ZHANG Jun, ZHANG Ankang, WANG Hui
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Cyberattack Graph Modeling for Visual Analytics
IEEE Access, 2023 Cybersecurity research demands continuous monitoring of the dynamic threat landscape to detect novel attacks. Researchers and security professionals often deploy honeypot networks to intercept and examine real attack data.
Matej Rabzelj +4 more
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Vulnerability Evaluation Algorithm Based on BNAG Model [PDF]
Jisuanji gongcheng, 2019 In order to accurately evaluate the vulnerability of computer network,a new evaluation algorithm is proposed by combining Bayesian network with attack graph.An attack graph model is constructed,which is named RSAG.On the basis of eliminating the loop in ...
WANG Hui, LOU Yalong, DAI Tianwang, RU Xinxin, LIU Kun
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Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph ...
Zhengyi Wang +4 more
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Attack Graph Obfuscation [PDF]
, 2017 Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers. Falsifying the information collected by the adversary post penetration may significantly slower lateral movement and increase the amount of noise generated ...
Hadar Polad, Rami Puzis, Bracha Shapira
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Robust Attack Graph Generation
CoRR, 2022 We present a method to learn automaton models that are more robust to input modifications. It iteratively aligns sequences to a learned model, modifies the sequences to their aligned versions, and re-learns the model. Automaton learning algorithms are typically very good at modeling the frequent behavior of a software system.
Dennis Mouwen, Sicco Verwer, Azqa Nadeem
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Transferable Graph Backdoor Attack
Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, 2022 Accepted by the 25th International Symposium on Research in Attacks, Intrusions, and ...
Shuiqiao Yang +7 more
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Portrait Analysis of Threat Intelligence for Attack Recognition [PDF]
Jisuanji gongcheng, 2020 New network attacks are getting more covert and persistent with a high proliferation,resulting in a sudden increase in the difficulty of attack recognition and detection.
YANG Peian, LIU Baoxu, DU Xiangyu
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Graph auto-encoder with a personalized PageRank for model inversion attack on graph neural networks [PDF]
PeerJ Computer ScienceModel inversion attack is the well-known privacy attack, in which the attacker infers the sensitive information of the input data by analyzing the output results of the model.
Ning Luan +6 more
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APT Attack Detection Based on Graph Convolutional Neural Networks
International Journal of Computational Intelligence Systems, 2023 Advanced persistent threat (APT) attacks are malicious and targeted forms of cyberattacks that pose significant challenges to the information security of governments and enterprises.
Weiwu Ren +6 more
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