Detection and prediction of the vulnerabilities in software systems based on behavioral analysis with machine learning

This study introduces Behavioral Analysis with Machine Learning (BAML), a novel approach designed to enhance cybersecurity by utilizing machine learning algorithms to detect and predict vulnerabilities in software systems based on behavioral data. BAML integrates both supervised and unsupervised learning techniques to analyze extensive datasets comprising system calls, network traffic, and user interactions. This method continuously monitors software operations, comparing observed behaviors against a machine-learned model to identify deviations that signal potential vulnerabilities. The effectiveness of BAML was assessed through a series of controlled experimental studies comparing its performance against traditional security testing methods such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Interactive Application Security Testing (IAST). BAML demonstrated superior accuracy with a true positive rate of 94%, the lowest false positive rate at 11%, and the highest code coverage of 93%. It also excelled in zero-day vulnerability detection and complex dependency analysis, showcasing its ability to adapt and respond to emerging threats dynamically. BAML offers significant advancements in the detection and prevention of software vulnerabilities. Its ability to learn from continuous data streams and adapt to new threats in real-time positions it as an essential tool for modern cybersecurity strategies, aligning well with Agile and DevOps practices. This proactive approach not only improves security but also reduces the costs and efforts associated with traditional reactive security measures.