Machine Learning Approach for Malware Detection Using Random Forest Classifier on Process List Data Structure

As computer systems have become an integral part of every organization, it is a big challenge to safeguard the computer systems from malicious activities which compromise not only the systems but also the data stored within. Traditional malware and rootkit detection using antivirus systems are not dynamic enough to capture the complex behavior of malware and its isolated activities. There are many signature-based malware detection techniques have been introduced, but enterprises as well as general users are still facing problems to get protection for their cyber systems against malware. Thus, it emphasizes the necessity of developing an efficient malware detection technique. In this research paper, we design a machine learning approach for malware detection using Random Forest classifier for the process list data extracted from Linux based virtual machine environment.