TCM-KNN algorithm for supervised network intrusion detection

Authors:
Yang Li;Bin-Xing Fang;Li Guo;You Chen
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate School of Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate School of Chinese Academy of Sciences, Beijing, China
Venue:
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Year:
2007

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Abstract

Intrusion detection is a hot topic related to information and national security. Supervised network intrusion detection has been an active and difficult research hotspot in the field of intrusion detection for many years. However, a lot of issues haven't been resolved successfully yet. The most important one is the loss of detection performance attribute to the difficulties in obtaining adequate attack data for the supervised classifiers to model the attack patterns, and the data acquisition task is always time-consuming which greatly relies on the domain experts. In this paper, we propose a novel network intrusion detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm. Experimental results on the well-known KDD Cup 1999 dataset demonstrate the proposed method is robust and more effective than the state-of-the-art intrusion detection method even provided with "small" dataset for training.