Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers
Machine learning is widely used to develop classifiers for security tasks. However, the robustness of these methods against motivated adversaries is uncertain. In this work, we propose a generic method to evaluate the robustness of classifiers under attack. The key idea is to stochastically manipulate a malicious sample to find a variant that preserves the malicious behavior but is classified as benign by the classifier. We present a general approach to search for evasive variants and report on results from experiments using our techniques against two PDF malware classifiers, PDFrate and Hidost. Our method is able to automatically find evasive variants for both classifiers for all of the 500 malicious seeds in our study. Our results suggest a general method for evaluating classifiers used in security applications, and raise serious doubts about the effectiveness of classifiers based on superficial features in the presence of adversaries.
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