  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
