mlsanityData to 7 Oct 2026

Attack and Defense of Deep Learning Models in the Field of Web Attack Detection

arXiv 2406.12605 ↗First posted 18 Jun 2024cs.LG cs.CRcited by 0

On the map: LLM safety and security → Backdoor and data poisoning attacks

Abstract (arXiv)

The challenge of WAD (web attack detection) is growing as hackers continuously refine their methods to evade traditional detection. Deep learning models excel in handling complex unknown attacks due to their strong generalization and adaptability. However, they are vulnerable to backdoor attacks, where contextually irrelevant fragments are inserted into requests, compromising model stability. While backdoor attacks are well studied in image recognition, they are largely unexplored in WAD. This paper introduces backdoor attacks in WAD, proposing five methods and corresponding defenses. Testing on textCNN, biLSTM, and tinybert models shows an attack success rate over 87%, reducible through fine-tuning. Future research should focus on backdoor defenses in WAD. All the code and data of this paper can be obtained at https://anonymous.4open.science/r/attackDefenceinDL-7E05

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security. Builds on 12 of its 24 references that are in the map. First posted on arXiv 18 Jun 2024.

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12 of 24 references.

References · 12

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  3. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  4. 2018Spectral Signatures in Backdoor Attackscited by 457
  5. 2018Detecting Backdoor Attacks on Deep Neural Networks by Activation Clusteringcited by 429
  6. 2020Backdoor Learning: A Surveycited by 412
  7. 2020ONION: A Simple and Effective Defense Against Textual Backdoor Attackscited by 231
  8. 2019A backdoor attack against LSTM-based text classification systemscited by 214
  9. 2020Clean-Label Backdoor Attacks on Video Recognition Modelscited by 162
  10. 2020Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identificationcited by 80
  11. 2019Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networkscited by 21
  12. 2018Network Traffic Anomaly Detection Using Recurrent Neural Networkscited by 11

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