JIS  Vol.3 No.1 , January 2012
A Multi-Stage Network Anomaly Detection Method for Improving Efficiency and Accuracy
ABSTRACT
Because of an explosive growth of the intrusions, necessity of anomaly-based Intrusion Detection Systems (IDSs) which are capable of detecting novel attacks, is increasing. Among those systems, flow-based detection systems which use a series of packets exchanged between two terminals as a unit of observation, have an advantage of being able to detect anomaly which is included in only some specific sessions. However, in large-scale networks where a large number of communications takes place, analyzing every flow is not practical. On the other hand, a timeslot-based detection systems need not to prepare a number of buffers although it is difficult to specify anomaly communications. In this paper, we propose a multi-stage anomaly detection system which is combination of timeslot-based and flow-based detectors. The proposed system can reduce the number of flows which need to be subjected to flow-based analysis but yet exhibits high detection accuracy. Through experiments using data set, we present the effectiveness of the proposed method.

Cite this paper
Y. Waizumi, H. Tsunoda, M. Tsuji and Y. Nemoto, "A Multi-Stage Network Anomaly Detection Method for Improving Efficiency and Accuracy," Journal of Information Security, Vol. 3 No. 1, 2012, pp. 18-24. doi: 10.4236/jis.2012.31003.
References
[1]   M. Roesch, “Snort-Lightweight Intrusion Detection for Networks,” LISA’99 Proceedings of the 13th USENIX Conference on System Administration, USENIX Association, Berkeley, 7-12 November 1999.

[2]   D. Anderson, T. F. Lunt, H. Javits, A. Tamaru and A. Baldes, “Detecting Unusual Program Behavior Using the Statistical Component of the Nextgeneration Intrusion Detection Expert System (NIDES),” Computer Science Laboratory SRI-CSL 95-06, May 1995.

[3]   R. Sekar, M. Bendre, D. Dhurjati and P. Bollineni, “A Fast Automaton-Based Method for Detecting Anomalous Program Behaviors,” Proceedings of the 2001 IEEE Symposium on Security and Privacy, Oakland, 2001.

[4]   Y. Sato, Y. Waizumi and Y. Nemoto, “Improving Accuracy of Network-Based Anomaly Detection Using Multiple Detection Modules,” Proceedings of IEICE Technical Report, NS2004-144, 2004, pp. 45-48.

[5]   P. Barford, J. Kline, D. Plonka and A. Ron, “A Signal Analysis of Network Traffic Anomalies,” Proceedings of ACM SIGCOMM Internet Measurement Workshop (IMW) 2002, Marseille, November 2002, pp. 71-82. doi:10.1145/637201.637210

[6]   T. Oikawa, Y. Waizumi, K. Ohta, N. Kato and Y. Nemoto, “Network Anomaly Detection Using Statistical Clustering Method,” Proceedings of IEICE Technical Report, NS2002-143, IN2002-87, CS2002-98, Oct, 2002 pp. 83-88.

[7]   Y. Waizumi, D. Kudo, N.Kato and Y. Nemoto, “A New Network Anomaly Detection Technique Based on Per-Flow and Per-Service Statistics,” Proceedings of International Conference on Computational Intelligence and Security, Xi’an, 15-19 December 2005, pp. 252-259.

[8]   A. Lakhina, M. Crovella and C. Diot, “Characterization of Network-Wide Anomalies in Traffic Flows,” Proceedings of the ACM/SIGCOMM Internet Measurement Conference, Taormina, 25-27 October 2004, pp. 201-206.

[9]   “DARPA Intrusion Detection Evaluation,” MIT Lincoln Labortory, Lincoln, 2011. http://www.ll.mit.edu/IST/ideval/index.html.

[10]   Inmon Corporation, “Flow Accuracy and Billing,” 2011. http:// www.inmon.com/pdf/sFlowBillilng.pdf.

[11]   N. Duffield, C. Lund and M. Thorup, “Properties and Prediction of Flow Statistics from Sampled Packet Streams,” Proceedings of ACM SIGCOMM Internet Measurement Workshop (IMW), Marseille, 6-8 November 2002. doi:10.1145/637201.637225

[12]   N. Duffield, C. Lund and M. Thorup, “Flow Sampling under Hard Resource Constraints,” Proceedings of ACM SIGMETRICS, New York, 10-14 June 2004.

[13]   “NeFlow,” 2011. http://www.cisco.com/warp/public/732/Tech/nmp/netflow/index.shtml.

[14]   P. Akritidis, K. Anagnostakis and E. P. Markatos, “Efficient Content-Based Detection of Zero-Day Worms,” Proceedings of the International Conference on Communications (ICC 2005), Seoul, 16-20 May 2005.

[15]   R. Lippmann, J. W. Haines, D. J. Fried, J. Korba and K. Das, “The 1999 DARPA Off-Line Intrusion Detection Evaluation,” Computer Networks, Vol. 34,No. 4, 2000, pp. 579- 595. doi:10.1016/S1389-1286(00)00139-0

[16]   P. Neumann and P. Porras, “Experience with EMERALD to DATE,” Proceedings of 1st USENIX Workshop on Intrusion Detection and Network Monitoring, Santa Clara, 9-12 April 1999, pp. 73-80.

[17]   G. Vigna, S. T. Eckmann and R. A. Kemmerer, “The STAT Tool Suite,” Proceedings of the 2000 DARPA Information Survivability Conference and Exposition (DISCEX), Hilton Head, 25-27 January 2000.

[18]   S. Jajodia, D. Barbara, B. Speegle and N. Wu, “Audit Data Analysis and Mining (ADAM),” 2000 http://www.isse.gmu.edu/dbarbara/adam.html

[19]   M. Tyson, P. Berry, N. Willams, D. Moran, D. Blei, “DERBI: Diagnosis, Explanation and Recovery from computer Break-Ins,” 2000.

[20]   M. Mahoney, “Network Traffic Anomaly Detection Based on Packet Bytes,” Proceedings of ACM-SAC, Melbourne, 9-12 March 2003, pp. 346-350.

 
 
Top