Rage Against the IDOR's - Using Machine Learning Models to Detect and Stop Authorization Bypass Vulnerabilities
Explore machine learning techniques for detecting and preventing authorization bypass vulnerabilities in web applications. Learn how to leverage open-source ML tools to identify successful attack attempts and block them before user data is compromised. Discover the challenges of typical detection approaches and understand how to predict authorization results using request signals and random forest models. Examine the limitations of backend signals and the potential of using server response data. Gain insights into creating bags of hashes, learning access patterns, and building and training models for effective attack detection and blocking. Understand the broader implications and considerations when implementing these security measures in real-world scenarios.