Summary of Attack and Defense Of Deep Learning Models in the Field Of Web Attack Detection, by Lijia Shi et al.
Attack and Defense of Deep Learning Models in the Field of Web Attack Detection
by Lijia Shi, Shihao Dong
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 abstract discusses the growing challenge of web attack detection (WAD), where hackers continually develop new methods to evade traditional detection. Deep learning models are effective in handling complex unknown attacks due to their strong generalization and adaptability, but they are vulnerable to backdoor attacks that compromise model stability. The paper proposes five methods for backdoor attacks in WAD, including defenses against these attacks. Testing on textCNN, biLSTM, and tinybert models shows an attack success rate over 87%, reducible through fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Web attack detection is a growing challenge as hackers refine their methods to evade traditional detection. Deep learning models can handle complex unknown attacks well, but they are vulnerable to backdoor attacks that compromise model stability. The paper introduces backdoor attacks in WAD and proposes ways to defend against them. This makes it harder for attackers to succeed. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Generalization
