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Analysing malware is important to understand how malicious software works andto develop appropriate detection and prevention methods. Dynamic analysis canovercome evasion techniques commonly used to bypass static analysis and provideinsights into malware runtime activities. Much research on dynamic analysisfocused on investigating machine-level information (e.g., CPU, memory, networkusage) to identify whether a machine is running malicious activities. Amalicious machine does not necessarily mean all running processes on themachine are also malicious. If we can isolate the malicious process instead ofisolating the whole machine, we could kill the malicious process, and themachine can keep doing its job. Another challenge dynamic malware detectionresearch faces is that the samples are executed in one machine without anybackground applications running. It is unrealistic as a computer typically runsmany benign (background) applications when a malware incident happens. Ourexperiment with machine-level data shows that the existence of backgroundapplications decreases previous state-of-the-art accuracy by about 20.12% onaverage. We also proposed a process-level Recurrent Neural Network (RNN)-baseddetection model. Our proposed model performs better than the machine-leveldetection model; 0.049 increase in detection rate and a false-positive ratebelow 0.1.

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