Paper
1 April 2003 Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders
Author Affiliations +
Abstract
With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today’s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today’s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen’s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abdi M. Abdi and Harold H. Szu "Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.502262
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Computer intrusion detection

Independent component analysis

Data modeling

Telecommunications

Computing systems

Machine learning

Neural networks

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