A Surveillance Spyware Detection System Based on Data Mining Methods

Author

Wang, Tzu-Yen ; Horng, Shi-Jinn ; Su, Ming-Yang ; Wu, Chin-Hsiung ; Wang, Peng-Chu ; Su, Wei-Zen

Author_Institution

Nat. Taiwan Univ. of Sci. & Technol., Taipei

fYear

0

fDate

0-0 0

Firstpage

3236

Lastpage

3241

Abstract

The problem of spyware is incredibly serious and exceeds anyone´s imagination. Combining static and dynamic analyses, we propose an integrated architecture to defend against surveillance spyware in this paper. Features extracted from both static and dynamic analyses are ranked according to their information gains. Then using top significant features we construct a Support Vector Machine (SVM) classifier for each client. In order to keep the classifier update-to-date, there is a machine playing as server to collect reports from all clients, retrain, and redistribute the new classifier to each client. Our surveillance spyware detection system (SSDS) has an overall accuracy rate up to 97.9% for known surveillance spywares and 96.4% for unknown ones.

Keywords

data mining; security of data; support vector machines; data mining methods; information gains; support vector machine classifier; surveillance spyware detection system; Advertising; Computer displays; Computer hacking; Computer science; Data mining; Feature extraction; Information analysis; Support vector machine classification; Support vector machines; Surveillance;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9487-9

Type

conf

DOI

10.1109/CEC.2006.1688720

Filename

1688720