Adversarial Malware Generation Using Binary Malware Samples

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Source arXiv

Machine Learning

Cryptography and Security

Adversarial Malware Generation Using Binary Malware Samples

A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries

We consider the problem of generating adversarial malware by a cyber-attacker where the attacker s task is to strategically modify certain bytes within existing binary malware files, so that the modified files are able to evade a machine learning-based malware classifier such as machine learning-based malware classifier.

We evaluate three recent adversarial malware generation techniques using binary malware samples drawn from a single, publicly available malware data set and compare their performances for evading a machine-learning based malware classifier called malconv.

Our results show that among the compared techniques, the most effective technique is the one that strategically modifies bytes in abinary s header.

Authors

Prithviraj Dasgupta, Zachariah Osman

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