Employing Program Semantics for Malware Detection

doi:10.1109/TIFS.2015.2469253

2015-12-01

PRE

Smita Naval *

Vijay Laxmi

Muttukrishnan Rajarajan

Manoj Singh Gaur

Mauro Conti

DOI：10.1109/TIFS.2015.2469253

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摘要

En 中文

In recent years, malware has emerged as a critical security threat. In addition, malware authors continue to embed numerous anti-detection features to evade the existing malware detection approaches. Against this advanced class of malicious programs, dynamic behavior-based malware detection approaches outperform the traditional signature-based approaches by neutralizing the effects of obfuscation and morphing techniques. The majority of dynamic behavior detectors rely on system-calls to model the infection and propagation dynamics of malware. However, these approaches do not account an important anti-detection feature of modern malware, i.e., system-call injection attack. This attack allows the malicious binaries to inject irrelevant and independent system-calls during the program execution thus modifying the execution sequences defeating the existing system-call-based detection. To address this problem, we propose an evasion-proof solution that is not vulnerable to system-call injection attacks. Our proposed approach characterizes program semantics using asymptotic equipartition property (AEP) mainly applied in information theoretic domain. The AEP allows us to extract information-rich call sequences that are further quantified to detect the malicious binaries. Furthermore, the proposed detection model is less vulnerable to call-injection attacks as the discriminating components are not directly visible to malware authors. We run a thorough set of experiments to evaluate our solution and compare it with the existing system-call-based malware detection techniques. The results demonstrate that the proposed solution is effective in identifying real malware instances.

Keyword:

Malware

malware detection

system-calls

semantically-relevant paths

system-call injection attacks