Defending Against Clean-Label Backdoor Attacks in Cybersecurity Machine Learning Models

A novel defense mechanism that leverages density-based clustering and iterative scoring to effectively mitigate clean-label backdoor attacks on machine learning models used in cybersecurity applications, without requiring access to clean training data or knowledge of the victim model architecture.