When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features
Hojjat Aghakhani(University of California, Santa Barbara), Fabio Gritti(University of California, Santa Barbara), Francesco Mecca(University of Turin), Martina Lindorfer(TU Wien), Stefano Ortolani(EURECOM), Davide Balzarotti(EURECOM), Giovanni Vigna(University of California, Santa Barbara), Christopher Kruegel(University of California, Santa Barbara)
Cited by 129Open Access
Abstract
Machine learning techniques are widely used in addition to signatures and heuristics to increase the detection rate of anti-malware software, as they automate the creation of detection models, making it possible to handle an ever-increasing number of new malware samples. In order to foil the analysis of anti-malware systems and evade detection, malware uses packing and other forms of obfuscation. However, few realize that benign applications use packing and obfuscation as well, to protect intellectual property and prevent license abuse.
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