EURASIP Journal on Advances in Signal Processing

Special issue on Signal Processing Applications in Network Intrusion 
Detection Systems

In recent years, network intrusion detection has attracted a lot of 
attention in the area of network security. Network intrusions cause threat 
and damage mainly in two ways. First, the intruders probe, gather, and 
deduce sensitive information about target hosts in an effort to gain 
unauthorized access to the target hosts and their networks. Second, the 
intruders inject huge waves of unwanted packets into the target networks, 
aiming to disrupt the normal communications carried on by the target 
networks. It is therefore very important to implement appropriate network 
intrusion detection systems (NIDSs) to monitor the network and detect the 
intrusion before it is too late.

Signal processing techniques have found applications in NIDSs because of 
their ability to detect novel intrusions and attacks, which cannot be 
achieved by signature-based NIDS. It has been shown that network traffic 
possesses the property of self-similarity. Therefore, the objective of 
NIDS based on signal processing techniques is to profile the pattern of 
normal network traffic or application-level behavior and model intrusions 
or unwanted traffic as anomalies. Wavelets, entropy analysis, and data 
mining techniques are examples in this regard. However, the major 
challenges of the signal processing-based approaches lie in the adaptive 
modeling of normal network traffic and the high false alarm rate due to 
the inaccuracy of the modeled normal traffic pattern. The emergence of a 
variety of wireless networks and the mobility of nodes in such networks 
only add to the complexity of the problems.

The goal of this special issue is to introduce state-of-the-art techniques 
and encourage research regarding various aspects in the application of 
signal processing techniques to network intrusion detection systems. In 
particular, the special issue encourages novel solutions that improve the 
accuracy and adaptivity of intrusion detection and addresses the 
automation of intrusion classification and correlation.

Topics of interest include (but are not limited to):

     * Data-mining-based IDS
     * Multirate filtering and wavelets
     * Monte Carlo methods integration
     * Anomalous network traffic modeling
     * Anomalous application-level behavior modeling
     * Performance analysis and evaluation
     * Real-time analysis techniques
     * Intrusion correlation
     * Automated detection and classification of intrusions and anomalies
     * Clustering-based IDS
     * Sampling techniques in intrusion detection
     * Data streaming algorithms for traffic analysis
     * Adaptive detection techniques
     * Data fusion in distributed intrusion detection

Authors should follow the EURASIP Journal on Advances in Signal Processing 
manuscript format described at the journal site Prospective authors should submit an 
electronic copy of their complete manuscript through the EURASIP Journal 
on Advances in Signal Processing Manuscript Tracking System at according to the following timetable:

     Manuscript Due	        September 1, 2007
     First Round of Reviews	December 1, 2007
     Publication Date	        March 1, 2008

Guest Editors:

Chin-Tser Huang, Department of Computer Science and Engineering, 
University of South Carolina, Columbia, SC 29208, USA

Rocky K. C. Chang, Department of Computing, The Hong Kong Polytechnic 
University, Hung Hom, Kowloon, Hong Kong

Polly Huang, Department of Electrical Engineering, National Taiwan 
University, Taipei, Taiwan