Zonglin Zhang¹ and Marisabel Chang², ¹USA, ²California State Polytechnic University, USA

In recent years, cybersecurity has grown increasingly salient in people's lives [8]. With the spread of various newmalware, the security risks of executable network installation packages are dramatically increasing, so problemspersist, rising with the growth of web users. This research work, aimed at a Crowdsourcing-based Analytical Engine for Virus and Malware Detection, prevents malware by examining MS Windows Portable Executable (PE) headers. YARA, a database from Kaggle, and data extracted from actual malware files were combined to createafinal dataset [9]. Comparing each section of the PE header to improve the detection accuracy, the final absoluteaccuracy is between 98% and 99%, and the front end displays the final prediction results through PythonGUI.

AI, Machine learning, Cybersecurity