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.

has been cited by the following article:

• TITLE: A Multi-Stage Network Anomaly Detection Method for Improving Efficiency and Accuracy

  AUTHORS: Yuji Waizumi, Hiroshi Tsunoda, Masashi Tsuji, Yoshiaki Nemoto

  KEYWORDS: Network Anomaly Detection; Timeslot-Based Analysis; Flow-Based Analysis; Multi-Stage Traffic Analysis; Flow Reduction

  JOURNAL NAME: Journal of Information Security, Vol.3 No.1, December 31, 2011

  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.