Malicious PE File Detection Using Machine Learning: An Analysis of Header Features

Portable Executable (PE) files serve as the foundation of the Windows operating system, containing executable code, object code, and Dynamic Link Libraries (DLLs). Their importance lies in their capacity to execute tasks and run programs within the Windows environment, rendering them essential to software execution. Comprehension of PE files is crucial in cybersecurity due to their prevalence and potential for harboring malicious software. Analysis of PE files enables researchers and analysts to uncover insights into their structure, behavior, and dependencies, which is essential for detecting and categorizing potential threats. PE file analysis provides critical information about a program's characteristics, facilitating the identification of indicators of compromise and potential security risks. Through examination of PE headers, sections, and attributes, analysts can gain insights into the file's functionality, dependencies, and potential security vulnerabilities. This knowledge equips cybersecurity professionals to develop robust detection and mitigation strategies, safeguarding systems and networks against cyber threats. In this paper, we examine the intricate details of PE files, utilizing tools such as PE-bear, PEinfo, CFF Explorer, and PEStudio to conduct comprehensive analysis. Our study employs machine learning techniques, specifically the Random Forest algorithm, to enhance malware detection capabilities. By preprocessing and augmenting the dataset with new executable files, we aim to enhance the efficacy of our analysis and provide valuable insights for cybersecurity research and defense strategies.