On the feasibility of adversarial machine learning in malware and network intrusion detection
Author:
Affiliation:
1. University of Modena and Reggio Emilia,Department of Engineering “Enzo Ferrari”,Modena,Italy
2. University of Bologna,Department of Computer Science and Engineering,Bologna,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9685017/9685042/09685709.pdf?arnumber=9685709
Reference31 articles.
1. DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems
2. Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
3. The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
4. Casting out Demons: Sanitizing Training Data for Anomaly Sensors
5. Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware
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1. ARGANIDS: a novel Network Intrusion Detection System based on adversarially Regularized Graph Autoencoder;Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing;2023-03-27
2. Efficient and interpretable SRU combined with TabNet for network intrusion detection in the big data environment;International Journal of Information Security;2022-12-30
3. Robustness Evaluation of Network Intrusion Detection Systems based on Sequential Machine Learning;2022 IEEE 21st International Symposium on Network Computing and Applications (NCA);2022-12-14
4. Comparison of Machine Learning-based anomaly detectors for Controller Area Network;2022 IEEE 21st International Symposium on Network Computing and Applications (NCA);2022-12-14
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