Machine Learning-based Vulnerability protections For Android Open...

This article introduces a machine-learning-driven Vulnerability Prevention (VP) framework that analyzes code changes at pre-submit time to detect likely security-inducing patches. Trained on years of AOSP data, the classifier uses novel feature sets—code complexity, review behavior, lifecycle signals, and line-level edits—to identify about 80% of vulnerable code submissions with 98% precision, enabling cheaper, earlier, and more scalable secure code reviews across large open-source projects.

Source: HackerNoon