Can I read Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data on EtoBox?

Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data by Garg, Aayush; Degiovanni, Renzo; Jimenez, Matthieu; Cordy, Maxime; Papadakis, Mike; Traon, Yves Le is a scholarly article available to read on EtoBox.

What is Learning from What We Know: How to Perform Vulnerability Prediction using Noisy Historical Data about?

Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the problem, and b) the inherently noisy historical data, i.e., most vulnerabilities are discovered much later than they are introduced. This misleads classifiers as they learn to recognize actual vulnerable components as non-vulnerable. To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed. We perform this by contrasting the known vulnerable, and their respective fixed components. This way, TROVON manages to learn from the things we know, i.e., vulnerabilities, hence reducing the effects of noisy and unbalanced data. We evaluate TROVON by comparing it with existing techniques on three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and Wireshark, with historical vulnerabilities that have been reporte

Author

Garg, Aayush; Degiovanni, Renzo; Jimenez, Matthieu; Cordy, Maxime; Papadakis, Mike; Traon, Yves Le

Published

2020

Language

EN