GMDH-based networks for intelligent intrusion detection

Network intrusion detection has been an area of rapid advancement in recent times. Similar advances in the field of intelligent computing have led to the introduction of several classification techniques for accurately identifying and differentiating network traffic into normal and anomalous. Group...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2013-08, Vol.26 (7), p.1731-1740
Main Authors: Baig, Zubair A., Sait, Sadiq M., Shaheen, AbdulRahman
Format: Article
Language:English
Subjects:
Classification
Computer information security
Feature ranking
Gain
GMDH
Intrusion
Machine learning
Network intrusion detection
Networks
Traffic engineering
Traffic flow
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Summary:Network intrusion detection has been an area of rapid advancement in recent times. Similar advances in the field of intelligent computing have led to the introduction of several classification techniques for accurately identifying and differentiating network traffic into normal and anomalous. Group Method for Data Handling (GMDH) is one such supervised inductive learning approach for the synthesis of neural network models. Through this paper, we propose a GMDH-based technique for classifying network traffic into normal and anomalous. Two variants of the technique, namely, Monolithic and Ensemble-based, were tested on the KDD-99 dataset. The dataset was preprocessed and all features were ranked based on three feature ranking techniques, namely, Information Gain, Gain Ratio, and GMDH by itself. The results obtained proved that the proposed intrusion detection scheme yields high attack detection rates, nearly 98%, when compared with other intelligent classification techniques for network intrusion detection.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2013.03.008