ML secrets detector beats regex

Short summary

ML classifier for secrets detection outperforms regex and entropy-only approaches by analyzing code context, particularly variable names. Combines pattern matching with entropy analysis and semantic scoring of variable names using Random Forest for interpretability. Catches both formatted secrets and low-entropy hardcoded credentials that traditional scanners miss.

• •Regex-only and entropy-only secrets scanners miss dangerous cases like hardcoded passwords with no distinctive format
• •Variable name context is the most important feature (0.28 importance) for distinguishing secrets from benign high-entropy strings
• •ML classifier using Random Forest combines pattern, entropy, and semantic variable-name scoring to catch secrets neither approach alone detects

Generated with AI, which can make mistakes.