NDLI: Classification of PE Files using Static Analysis

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Classification of PE Files using Static Analysis

Content Provider	ACM Digital Library
Author	Saini, Ashish Bansal, Divya Gandotra, Ekta Sofat, Sanjeev
Abstract	Malware is one of the most terrible and major security threats facing the Internet today. Anti-malware vendors are challenged to identify, classify and counter new malwares due to the obfuscation techniques being used by malware authors. In this paper, we present a simple, fast and scalable method of differentiating malwares from cleanwares on the basis of features extracted from Windows PE files. The features used in this work are Suspicious Section Count and Function Call Frequency. After automatically extracting features of executables, we use machine learning algorithms available in WEKA library to classify them into malwares and cleanwares. Our experimental results provide an accuracy of over 98% for a data set of 3,087 executable files including 2,460 malwares and 627 cleanwares. Based on the results obtained, we conclude that the Function Call Frequency feature derived from the static analysis method plays a significant role in distinguishing malware files from benign ones.
Starting Page	429
Ending Page	433
Page Count	5
File Format	PDF
ISBN	9781450330336
DOI	10.1145/2659651.2659679
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Classification Machine learning Static malware analysis
Content Type	Text
Resource Type	Article

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