Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection

• URL: http://arxiv.org/abs/2310.18165v1
• Date: Fri, 27 Oct 2023 14:17:35 GMT
• Title: Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection
• Authors: Baskoro Adi Pratomo, Toby Jackson, Pete Burnap, Andrew Hood, Eirini Anthi
• Abstract summary: Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis. A malicious machine does not necessarily mean all running processes on the machine are also malicious. The existence of background applications decreases previous state-of-the-art accuracy by about 20.12% on average.
• Score: 2.812395851874055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide insights into malware runtime activities. Much research on dynamic analysis focused on investigating machine-level information (e.g., CPU, memory, network usage) to identify whether a machine is running malicious activities. A malicious machine does not necessarily mean all running processes on the machine are also malicious. If we can isolate the malicious process instead of isolating the whole machine, we could kill the malicious process, and the machine can keep doing its job. Another challenge dynamic malware detection research faces is that the samples are executed in one machine without any background applications running. It is unrealistic as a computer typically runs many benign (background) applications when a malware incident happens. Our experiment with machine-level data shows that the existence of background applications decreases previous state-of-the-art accuracy by about 20.12% on average. We also proposed a process-level Recurrent Neural Network (RNN)-based detection model. Our proposed model performs better than the machine-level detection model; 0.049 increase in detection rate and a false-positive rate below 0.1.

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