Anomaly based intrusion detection through temporal classification


Ooi, Shih Yin and Tan, Shing Chiang and Cheah, Wooi Ping (2014) Anomaly based intrusion detection through temporal classification. In: Neural Information Processing. Lecture Notes in Computer Science . Springer International Publishing, pp. 612-619. ISBN 978-3-319-12643-2

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Many machine learning techniques have been used to classify anomaly-based network intrusion data, encompassing from single classifier to hybrid or ensemble classifiers. A nonlinear temporal data classification is proposed in this work, namely Temporal-J48, where the historical connection records are used to classify the attack or predict the unseen attack. With its tree-based architecture, the implementation is relatively simple. The classification information is readable through the generated temporal rules. The proposed classifier is tested on 1999 KDD Cup Intrusion Detection dataset from UCI Machine Learning Repository. Promising results are reported for denial-of-service (DOS) and probing attack types.

Item Type: Book Section
Additional Information: Book Subtitle: 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part III
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 22 Jan 2015 09:57
Last Modified: 22 Jan 2015 09:57


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