Evading Machine Learning Classifiers

21 December 2015

Today we’re releasing our paper on evading machine learning classifiers:

Weilin Xu, Yanjun Qi, and David Evans. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers Network and Distributed System Security Symposium (NDSS). San Diego, CA. 21-24 February 2016. [PDF, 15 pages]

The main idea behind the paper is to explore how an adaptive adversary can evade a machine learning-based malware classifier by using techniques from genetic programming to automatically explore the space of potential evasive variants.

In a case study using two PDF malware classifiers as targets, we find that it is possible to automatically find evasive variants (that is, variants that preserve the desired malicious behavior while being (mis)classified as benign) for all 500 seeds in our test dataset.

Weilin Xu will present the work at the Network and Distributed Systems Security Symposium in San Diego in February.

For more, see EvadeML.org or the full paper (PDF).