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Báo cáo hóa học: " Research Article A Novel Approach to Detect Network Attacks Using G-HMM-Based Temporal Relations between Internet Protocol Packets"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Novel Approach to Detect Network Attacks Using G-HMM-Based Temporal Relations between Internet Protocol Packets | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2011 Article ID 210746 14 pages doi 10.1155 2011 210746 Research Article A Novel Approach to Detect Network Attacks Using G-HMM-Based Temporal Relations between Internet Protocol Packets Taeshik Shon 1 Kyusuk Han 2 James J. Jong Hyuk Park 3 and Hangbae Chang4 1 Division of Information and Computer Engineering College of Information Technology Ajou University Suwon 443-749 Republic of Korea 2 Department of Information and Communication Engineering Korea Advanced Institute of Science and Technology 119 Munjiro Yuseong-gu Daejeon 305-701 Republic of Korea 3 Department of Computer Science and Engineering SeoulNational University of Science and Technology 172 Gongneung 2-Dong Nowon Seoul 139-743 Republic of Korea 4 Department of Business Administration Daejin University San 11-1 Sundan-Dong Pocheon-Si Gyunggi-Do 487-711 Republic of Korea Correspondence should be addressed to Hangbae Chang hbchang@daejin.ac.kr Received 20 August 2010 Accepted 19 January 2011 Academic Editor Binod Vaidya Copyright 2011 Taeshik Shon et al. 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. 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