A Novel Approach for PE Malware Detection using Random Forest Algorithm and Prevention

In light of the increase in malware activity spurred on by the quick development of technology, security has evolved to be a significant concern and now threatens the safety and security of both computer systems and stakeholders. One of the most urgent concerns is safeguarding the data from fraudulent attempts in order to preserve stakeholder security, notably that of end users. Malware is a collection of harmful software code, scripts, dynamic content, or intrusive software that is meant to damage legitimate computer programs, mobile, or web apps. This study has highlighted the portable and executable malware detection using machine learning approach. A study found that the novice users will not be able to differentiate the dangerous and good software. Here, the machine learning based Random Forest (RF) classification method is used by examining the components of Portable executable file. With this detection, an accuracy value of 99% is achieved through classification. Several techniques are available to identify the malware activity that make use of cutting-edge technologies like Machine Learning (ML).