Malware Dynamic Analysis Evasion Techniques: A Survey
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Abstract (arXiv)
The Cyber world is plagued with ever-evolving malware that readily infiltrates all defense mechanisms, operates 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 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 paper, 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 hold against different types of detection and analysis approach. Our observations attest that evasive behavior is mostly interested in 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 pursuit of more generic defensive strategies with emphasis on path exploration techniques that have the potential to thwart all the evasive tactics.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2.
6 papers.
- 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learningcited by 6
- 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
- 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelinescited by 4
- 2022Parallel Instance Filtering for Malware Detectioncited by 1
- 2021Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisationcited by 0
- 2022Malware Analysis with Symbolic Execution and Graph Kernelcited by 0
- 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems31 Oct 2024
- 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines23 May 2024
- 2022Parallel Instance Filtering for Malware Detection28 Jun 2022
- 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning25 May 2022
- 2022Malware Analysis with Symbolic Execution and Graph Kernel12 Apr 2022
- 2021Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation8 Sep 2021