Web Attack

Web attack detection aims to identify and mitigate malicious activities targeting web applications, a critical area given the increasing sophistication of attacks. Current research focuses on improving the accuracy and efficiency of web application firewalls (WAFs) using machine learning techniques, such as One-Class SVMs and adapting rule-based systems like ModSecurity with learned weights, to reduce false positives while enhancing detection rates. A significant challenge involves the vulnerability of deep learning models to adversarial attacks, prompting research into robust defenses against backdoor attacks and the development of secure code generation models. These advancements are crucial for enhancing online security and protecting web applications from various threats.