Adversarial SQLi Detection Using Character-Level CNN and Reinforcement Learning

Adversarial SQL injection (SQLi, a technique where attackers modify their attacks based on feedback from a Web Application Firewall to bypass it) has become a serious threat, with automated tools like AdvSQLi and GPTFuzzer making it easier to find vulnerabilities. The paper proposes a hybrid defense system combining Character-Level CNN (a neural network that analyzes attack payloads character-by-character to find harmful patterns) and Reinforcement Learning (a type of AI training that learns through trial and feedback) to detect these advanced attacks, showing that this approach can catch malicious patterns even when attackers try to disguise their payloads.