Software vulnerabilities are prevalent across all systems that are built using source codes, causing a variety of problems including deadlock, hacking or even system failures. Thus, early predictions of vulnerabilities are critical for security software systems.
Software vulnerabilities are prevalent across all systems that are built using source codes, causing a variety of problems including deadlock, hacking or even system failures. Thus, early predictions of vulnerabilities are critical for security software systems. New research from Monash University presents the most effective approach to accurately predict vulnerabilities in software code and strengthen cybersecurity. To help combat this, Faculty of Information Technology experts developed the 'LineVul' approach, and found it increased accuracy in predicting software vulnerabilities by more than 300 percent while spending only half the usual amount of time and effort, when compared to current best-in-class prediction tools. LineVul is also able to guard against the top 25 most dangerous and common weaknesses in source codes, and can be applied broadly to strengthen cybersecurity across any application built with source code. Research co-author Dr Chakkrit Tantithamthavorn, from the Faculty of Information Technology (IT), said standard software programs contain millions to billions of lines of code and it often takes a significant amount of time to identify and rectify vulnerabilities. "Current state-of-the-art machine learning-based vulnerability prediction tools are still inaccurate and are only able to identify general areas of weakness in the source codes," Dr Tantithamthavorn said.
TO READ THIS ARTICLE, CREATE YOUR ACCOUNT
And extend your reading, free of charge and with no commitment.
