NDLI: Detection of Spyware by Mining Executable Files

Content Provider	IEEE Xplore Digital Library
Author	Shahzad, R.K. Haider, S.I. Lavesson, N.
Copyright Year	2010
Abstract	Spyware represents a serious threat to confidentiality since it may result in loss of control over private data for computer users. This type of software might collect the data and send it to a third party without informed user consent. Traditionally two approaches have been presented for the purpose of spyware detection: Signature-based Detection and Heuristic-based Detection. These approaches perform well against known Spyware but have not been proven to be successful at detecting new spyware. This paper presents a Spyware detection approach by using Data Mining (DM)technologies. Our approach is inspired by DM-based malicious code detectors, which are known to work well for detecting viruses and similar software. However, this type of detector has not been investigated in terms of how well it is able to detect spyware. We extract binary features, called n-grams, from both spyware and legitimate software and apply five different supervised learning algorithms to train classifiers that are able to classify unknown binaries by analyzing extracted n-grams. The experimental results suggest that our method is successful even when the training data is scarce.
Starting Page	295
Ending Page	302
File Size	304158
Page Count	8
File Format	PDF
ISBN	9781424458790
e-ISBN	9781424458806
DOI	10.1109/ARES.2010.105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-02-15
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Availability Computer viruses Software performance Feature Extraction Data Mining Spyware Detection Data mining Application software Computer displays Detectors Feature extraction Software systems Malicious Code Viruses (medical)
Content Type	Text
Resource Type	Article

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