A STUDY ON DIGITAL FORENSICS USING VARIOUS ALGORITHMS FOR MALWARE DETECTION
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Abstract
Malware Detection is a field of Digital Forensics which involves detection of known and unknown malware by various methods. Detection of real-time malware becomes a big challenge, the research done in the field has shown the advancement achieved in malware detection system designs and implementations. Although each malware is unique, malware has some common behavioral characteristics which can be examined and used for malware detection. This paper has a survey and analysis of various research works on Malware Detection using behavior characteristics and also introduces its problems and issues. Finally, we have compared various machine learning algorithms which can be used for most effective malware detection process. The implementation and the results of the study show that the Random Forest algorithm is a most efficient algorithm for detection of malicious files in any system.
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