Environment–Reactive Malware Behavior: Detection and Categorization

Abstract

Present malicious threats have been consolidated in past few years by incorporating diverse stealthy techniques. Detecting these malwares on the basis of their dynamic behavior has become a potential approach as it suppresses the shortcomings of static approaches raised due to the obfuscated malware binaries. Additionally, existing behavior based malware detection approaches are resilient to zero–day malware attacks. These approaches rely on isolated analysis environment to monitor and capture the run–time malware behavior. Malware bundled with environment–aware payload may degrade detection accuracy of such approaches. These malicious programs detect the presence of execution environment and thus inspite of having their malicious payload they mimic a benign behavior to avoid detection. In this paper, we have presented an approach using system–calls to identify a malware on the basis of their malignant and environment–reactive behavior. The proposed approach offers an automated screening mechanism to segregate malware samples on the basis of aforementioned behaviors. We have built a decision model which is based on multi–layer perceptron learning with back propagation algorithm. Our proposed model decides the candidacy of a sample to be put into one of the four classes (clean, malignant, guest–crashing and infinite–running). Clean behavior denotes benign sample and rest of the behaviors denote the presence of malware sample. The proposed technique has been evaluated with known and unknown instances of real malware and benign programs.