Malware Detection Through Decision Tree Classifier
Malware incidents cost organizations and industries billions of dollars every year. In a 2012 worldwide survey on the financial impacts of malware, more than 2,600 business leaders and IT security practitioners were interviewed. The first part of this paper is devoted to a brief introduction, terminology and a comparison between different methods of preventing and detecting malware. The second portion of this paper presents a new method for classifying malicious files versus normal ones. The authors’ approach is based on differences between assembly op-code frequencies in malware and benign classes. They have also utilized decision tree algorithms to simplify the classification.
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