Adversarial SQLi Detection Using Character-Level CNN and Reinforcement Learning

Summary

Researchers propose a hybrid detector that combines an enhanced Character-Level CNN (CLCNN) with a reinforcement learning module to catch adversarial SQL injection payloads that evade Web Application Firewalls. The CLCNN extracts multi-scale features, and the RL module de-obfuscates payloads where the CLCNN's confidence is low. The paper reports that the CLCNN resists tools such as AdvSQLi, RAT, and GPTFuzzer without prior knowledge, and that RL-based adversarial training raises Recall with minimal loss on standard SQLi datasets.

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