ENHANCING SECURITY IN PYTHON DEVELOPMENT ENVIRONMENTS: DETECTION AND ANALYSIS OF THE EMBEDDED MALWARE

Abstract

In our below research, we execute a methodology that filters out file-based and fileless malware from a dataset of malicious Python libraries. We have developed an approach to identify malicious code within these Python libraries. Our approach makes use of static and dynamic analysis in addition to behavioral analysis to accurately distinguish between file-based and fileless Python modules. Further, we analyze the malicious packages that execute fileless processes. This research aims to contribute towards enhancing the security of Python development ecosystems by providing developers and security professionals with tools to identify and mitigate malware threats effectively. We have also discussed some background information on fileless malwares.