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Spyware Detection Using Data Mining
Authors: Karishma Pandey, Madhura Naik, Junaid Qamar,Mahendra Patil
Number of views: 682
The systems connected to the network are vulnerable to many malicious programs which threatens the
confidentiality, integrity and availability of a system. Many malicious programs such as viruses, worms, trojan horses, adware,
scareware exists. A new malicious program has gained momentum known as spyware. Traditional techniques such as
Signature-based Detection and Heuristic-based Detection have not performed well in detecting Spyware. Based on the recent
studies it has been proven that data mining techniques yield better results than these traditional techniques. This paper presents
detection of spyware using data mining approach. Here binary feature extraction takes place from executable files, which is
then followed by feature reduction process so that it can be used as training set to generate classifiers. Hence, the generated
classifiers classify new and previously unseen binaries as benign files or spywares.
Keywords — Malicious Code, Feature Extraction, N-Gram, CFBE (Common Feature-based Extraction), FBFE
(Frequency-based Feature Extraction), Data Mining, Spyware, Naïve Bayes Classification Algorithm
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