Evading Deep Reinforcement Learning-based Network Intrusion Detection with Adversarial Attacks
In: ARES 2022: The 17th International Conference on Availability, Reliability and Security ; https://hal.science/hal-04025173 ; ARES 2022: The 17th International Conference on Availability, 2022
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Zugriff:
International audience ; An Intrusion Detection System (IDS) aims to detect attacks conducted over computer networks by analyzing traffic data. Deep Reinforcement Learning (Deep-RL) is a promising lead in IDS research, due to its lightness and adaptability. However, the neural networks on which Deep-RL is based can be vulnerable to adversarial attacks. By applying a well-computed modification to malicious traffic, adversarial examples can evade detection. In this paper, we test the performance of a state-of-the-art Deep-RL IDS agent against the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) adversarial attacks. We demonstrate that the performance of the Deep-RL detection agent is compromised in the face of adversarial examples and highlight the need for future Deep-RL IDS work to consider mechanisms for coping with adversarial examples.
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Evading Deep Reinforcement Learning-based Network Intrusion Detection with Adversarial Attacks
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Autor/in / Beteiligte Person: | Merzouk, Mohamed Amine ; Delas, Joséphine ; Neal, Christopher ; Cuppens, Frédéric ; Boulahia-Cuppens, Nora ; Yaich, Reda ; École Polytechnique de Montréal (EPM) ; SystemX, IRT |
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Zeitschrift: | ARES 2022: The 17th International Conference on Availability, Reliability and Security ; https://hal.science/hal-04025173 ; ARES 2022: The 17th International Conference on Availability, 2022 |
Veröffentlichung: | HAL CCSD ; ACM, 2022 |
Medientyp: | Konferenz |
DOI: | 10.1145/3538969.3539006 |
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