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Lightweight and Scalable Intrusion Trace Classification Using Interelement Dependency Models Suitable for Wireless Sensor Network Environment
Author
Source
International Journal of Distributed Sensor Networks
Issue
Vol. 2013, Issue - (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-08
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
We present a lightweight and scalable method for classifying network and program traces to detect system intrusion attempts.
By employing interelement dependency models to overcome the independence violation problem inherent in the Naive Bayes learners, our method yields intrusion detectors with better accuracy.
For efficient and lightweight counting of n-gram features without losing accuracy, we use a k-truncated generalized suffix tree (k-TGST) for storing n-gram features.
The k-TGST storage mechanism enables us to scale up the classifiers, which cannot be easily achieved by Support-Vector-Machine- (SVM-) based methods that require implausible computing power and resources for accuracy.
Experimental results on a set of practical benchmark datasets show that our method is scalable up to 20-gram with consistent accuracy comparable to SVMs.
American Psychological Association (APA)
Kang, Dae-Ki. 2013. Lightweight and Scalable Intrusion Trace Classification Using Interelement Dependency Models Suitable for Wireless Sensor Network Environment. International Journal of Distributed Sensor Networks،Vol. 2013, no. -, pp.1-10.
https://search.emarefa.net/detail/BIM-506879
Modern Language Association (MLA)
Kang, Dae-Ki. Lightweight and Scalable Intrusion Trace Classification Using Interelement Dependency Models Suitable for Wireless Sensor Network Environment. International Journal of Distributed Sensor Networks Vol. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-506879
American Medical Association (AMA)
Kang, Dae-Ki. Lightweight and Scalable Intrusion Trace Classification Using Interelement Dependency Models Suitable for Wireless Sensor Network Environment. International Journal of Distributed Sensor Networks. 2013. Vol. 2013, no. -, pp.1-10.
https://search.emarefa.net/detail/BIM-506879
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-506879