Open Access

A Novel Approach to Detect Network Attacks Using G-HMM-Based Temporal Relations between Internet Protocol Packets

  • Taeshik Shon1,
  • Kyusuk Han2,
  • James J. (Jong Hyuk) Park3 and
  • Hangbae Chang4Email author
EURASIP Journal on Wireless Communications and Networking20112011:210746

Received: 20 August 2010

Accepted: 19 January 2011

Published: 10 March 2011


This paper introduces novel attack detection approaches on mobile and wireless device security and network which consider temporal relations between internet packets. In this paper we first present a field selection technique using a Genetic Algorithm and generate a Packet-based Mining Association Rule from an original Mining Association Rule for Support Vector Machine in mobile and wireless network environment. Through the preprocessing with PMAR, SVM inputs can account for time variation between packets in mobile and wireless network. Third, we present Gaussian observation Hidden Markov Model to exploit the hidden relationships between packets based on probabilistic estimation. In our G-HMM approach, we also apply G-HMM feature reduction for better initialization. We demonstrate the usefulness of our SVM and G-HMM approaches with GA on MIT Lincoln Lab datasets and a live dataset that we captured on a real mobile and wireless network. Moreover, experimental results are verified by -fold cross-validation test.

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Authors’ Affiliations

Division of Information and Computer Engineering, College of Information Technology, Ajou University
Department of Information and Communication Engineering, Korea Advanced Institute of Science and Technology
Department of Computer Science and Engineering, Seoul National University of Science and Technology
Department of Business Administration, Daejin University


© Taeshik Shon et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.