Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood.
Thus, in this paper, we propose a novel technique to provide automatic repairs of integer overflows in C source code.
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies.
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files.
Cryptography and Security