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

翻译：随着黑客不断改进其规避传统检测的方法，Web攻击检测（WAD）面临的挑战日益严峻。深度学习模型凭借其强大的泛化能力和适应性，在处理复杂的未知攻击方面表现出色。然而，它们容易受到后门攻击的影响，即在请求中插入上下文无关的片段，从而损害模型的稳定性。尽管后门攻击在图像识别领域已得到充分研究，但在WAD领域却鲜有探索。本文在WAD中引入后门攻击，提出了五种攻击方法及相应的防御策略。在textCNN、biLSTM和tinybert模型上的测试表明，攻击成功率超过87%，但可通过微调降低。未来的研究应重点关注WAD中的后门防御。本文的所有代码和数据均可通过 https://anonymous.4open.science/r/attackDefenceinDL-7E05 获取。