According to our survey of the machine learning for vulnerability detection (ML4VD) literature published in the top Software Engineering conferences, every paper in the past 5 years defines ML4VD as a binary classification problem: Given a function, does it contain a security flaw? ...
Recent results of machine learning for automatic vulnerability detection (ML4VD) have been very promising. Given only the source code of a function f, ML4VD techniques can decide if f contains a security flaw with up to 70% accuracy ...
Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of more robust models hinges on a rigorous understanding ...
