A Method of Malware Detection Using Machine Learning Algorithm

The current challenge, in the cyber world is to identify and categorize software. To tackle this machine learning can be utilized to detect and classify malware by analyzing patterns, behaviors and characteristics. However the evolving nature of malware presents obstacles for traditional detection methods that rely on signatures. To overcome this issue a smart system for detecting malware is introduced in this paper. This system harnesses the power of machine learning algorithms by examining a dataset comprising both malware samples and legitimate software. It extracts features such as file metadata, byte level information, system calls and network traffic. The results obtained from these experiments demonstrate the potential of machine learning techniques, in combating malware while underscoring their significance within cybersecurity strategies.