Investigation of Malicious Portable Executable File Detection on the Network using Supervised Learning Techniques Rushabh Vyas, Xiao Luo, Nichole McFarland, Connie Justice Department of Information and Technology, Purdue School of Engineering and Technology IUPUI, Indianapolis, IN, USA 46202 Emails:
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[email protected] Abstract—Malware continues to be a critical concern for that random forest learning technique achieved best detection everyone from home users to enterprises. Today, most devices are performance than the other three learning techniques. The connected through networks to the Internet. Therefore, malicious achieved detection rate and false alarm rate on experimental code can easily and rapidly spread. The objective of this paper is to examine how malicious portable executable (PE) files can be data set were 98.7% and 1.8% respectively. We compared detected on the network by utilizing machine learning algorithms. the performances of the four learning techniques on four The efficiency and effectiveness of the network detection rely types of PE malware - backdoors, viruses, worms and trojans. on the number of features and the learning algorithms. In this The results showed that the same learning technique show work, we examined 28 features extracted from metadata, packing, the similar performance on different types of malwares. It imported DLLs and functions of four different types of PE files for malware detection. The returned results showed that the demonstrated the features that are extracted from the files have proposed system can achieve 98.7% detection rates, 1.8% false no bias for a specific type of malware.