Employing attack graphs for intrusion detection

Author:

Capobianco Frank1,George Rahul1,Huang Kaiming1,Jaeger Trent1,Krishnamurthy Srikanth2,Qian Zhiyun2,Payer Mathias3,Yu Paul4

Affiliation:

1. Penn State University

2. UC Riverside

3. Ecole Polytechnique Fédérale de Lausanne

4. U.S. Army Combat Capabilities Development Command Army Research Laboratory

Funder

National Science Foundation

ARL Cyber Security CRA

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GNN-IDS: Graph Neural Network based Intrusion Detection System;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30

2. Triaging Android Systems Using Bayesian Attack Graphs;2023 IEEE Secure Development Conference (SecDev);2023-10-18

3. Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs;Cybersecurity;2023-09-01

4. Unsupervised Abnormal Traffic Detection through Topological Flow Analysis;2022 14th International Conference on Communications (COMM);2022-06-16

5. IOTA: A Framework for Analyzing System-Level Security of IoTs;2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI);2022-05

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