Summary of Deep Learning For Network Anomaly Detection Under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation, by D’jeff K. Nkashama et al.
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
by D’Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani, Jean-Charles Verdier, Pierre-Martin Tardif, Marc Frappier, Froduald Kabanza
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study evaluates the robustness of six unsupervised deep learning algorithms against data contamination for network anomaly detection in cybersecurity. The authors demonstrate significant performance degradation in state-of-the-art models when exposed to contaminated data, highlighting the need for self-protection mechanisms. To mitigate this vulnerability, an enhanced auto-encoder is proposed with a constrained latent representation, which exhibits improved resistance to data contamination compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has become a crucial tool for detecting network anomalies in cybersecurity. But what happens when training sets contain attack-related data? The study shows that top-performing algorithms suffer from significant performance degradation when exposed to contaminated data. To fix this problem, researchers propose an enhanced auto-encoder that makes it harder for normal data to get mixed with attack data. |
Keywords
* Artificial intelligence * Anomaly detection * Deep learning * Encoder * Unsupervised
