AdaBoost-Based Algorithm for Network Intrusion Detection

Abstract--Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for a...

• Type: Journal
• Year: 2008
• Where: TSMC
• Authors: Weiming Hu, Wei Hu, Stephen J. Maybank
• Keywords: Algorithms, Intrusion Detection, TSMC 2008, Weak Classifiers