Robustness Evaluation of Deep Unsupervised Learning Algorithms for
Intrusion Detection Systems
- URL: http://arxiv.org/abs/2207.03576v2
- Date: Mon, 30 Oct 2023 15:48:39 GMT
- Title: Robustness Evaluation of Deep Unsupervised Learning Algorithms for
Intrusion Detection Systems
- Authors: D'Jeff Kanda Nkashama, Arian Soltani, Jean-Charles Verdier, Marc
Frappier, Pierre-Martin Tardif, Froduald Kabanza
- Abstract summary: This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data.
Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, advances in deep learning have been observed in various fields,
including computer vision, natural language processing, and cybersecurity.
Machine learning (ML) has demonstrated its ability as a potential tool for
anomaly detection-based intrusion detection systems to build secure computer
networks. Increasingly, ML approaches are widely adopted than heuristic
approaches for cybersecurity because they learn directly from data. Data is
critical for the development of ML systems, and becomes potential targets for
attackers. Basically, data poisoning or contamination is one of the most common
techniques used to fool ML models through data. This paper evaluates the
robustness of six recent deep learning algorithms for intrusion detection on
contaminated data. Our experiments suggest that the state-of-the-art algorithms
used in this study are sensitive to data contamination and reveal the
importance of self-defense against data perturbation when developing novel
models, especially for intrusion detection systems.
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