Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence.

Dynamic malware analysis plays a pivotal role in modern cybersecurity, offering insights into malware behavior through dynamic execution and network traffic analysis. In this study, we present a comprehensive approach to dynamic malware analysis using a sandbox environment and network traffic logs. Our methodology involves the extraction of relevant features from network traffic captured in pcap files. We conducted experiments using a virtualized Oracle VirtualBox environment, where benign and malicious software samples were executed within a Windows virtual machine controlled by Python scripts. For network emulation, we utilized tools from the REMnux distribution, including InetSim and FakeDNS, to simulate realistic network interactions during malware execution. The collected pcap data underwent preprocessing and feature extraction to capture essential behavioral patterns and network indicators. Machine learning and artificial intelligence models were developed to classify malware based on these extracted features. Our findings underscore the efficacy of dynamic analysis coupled with machine learning in detecting and classifying malware variants based on their network behavior. This research contributes to advancing techniques for real-time threat detection and response in cybersecurity, emphasizing the importance of dynamic malware analysis in mitigating evolving cyber threats.