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Malware Dynamic Analysis Evasion Techniques: A Survey

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Published:14 November 2019Publication History
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Abstract

The cyber world is plagued with ever-evolving malware that readily infiltrate all defense mechanisms, operate viciously unbeknownst to the user, and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such malware provides a leverage to effectively combat them. This understanding is pursued often through dynamic analysis which is conducted manually or automatically. Malware authors accordingly, have devised and advanced evasion techniques to thwart or evade these analyses. In this article, we present a comprehensive survey on malware dynamic analysis evasion techniques. In addition, we propose a detailed classification of these techniques and further demonstrate how their efficacy holds against different types of detection and analysis approaches.

Our observations attest that evasive behavior is mostly concerned with detecting and evading sandboxes. The primary tactic of such malware we argue is fingerprinting followed by new trends for reverse Turing test tactic which aims at detecting human interaction. Furthermore, we will posit that the current defensive strategies, beginning with reactive methods to endeavors for more transparent analysis systems, are readily foiled by zero-day fingerprinting techniques or other evasion tactics such as stalling. Accordingly, we would recommend the pursuit of more generic defensive strategies with an emphasis on path exploration techniques that has the potential to thwart all the evasive tactics.

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  1. Malware Dynamic Analysis Evasion Techniques: A Survey

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      • Published in

        cover image ACM Computing Surveys
        ACM Computing Surveys  Volume 52, Issue 6
        November 2020
        806 pages
        ISSN:0360-0300
        EISSN:1557-7341
        DOI:10.1145/3368196
        • Editor:
        • Sartaj Sahni
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        Copyright © 2019 ACM

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        Publication History

        • Published: 14 November 2019
        • Accepted: 1 September 2019
        • Revised: 1 June 2019
        • Received: 1 November 2018
        Published in csur Volume 52, Issue 6

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