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Found 4 papers, 2 papers with code
Support Vector Machines under Adversarial Label Contamination
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.
IntRepair: Informed Repairing of Integer Overflows
Thus, in this paper, we propose a novel technique to provide automatic repairs of integer overflows in C source code.
Software Engineering
Is feature selection secure against training data poisoning?
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.
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files.
Cryptography and Security
