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.