An Incremental Self-Improvement Hybrid Intrusion Detection System Mahbod Tavallaee, Wei Lu, and Ali A. Ghorbani Faculty of Computer Science, UNB Fredericton.

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An Incremental Self-Improvement Hybrid Intrusion Detection System Mahbod Tavallaee, Wei Lu, and Ali A. Ghorbani Faculty of Computer Science, UNB Fredericton 1. Introduction In order to overcome the challenges of anomaly detection and keep the advantages of misuse detection, some researchers have proposed the idea of hybrid detection. This way, the system will achieve the advantage of misuse detection to have a high detection rate on known attacks as well as the ability of anomaly detectors in detecting unknown attacks. b) The Fusing Algorithm 2. Motivation There exist two important issues that make the implementation of the hybrid systems cumbersome: All anomaly-based methods need a completely labeled and up-to-date training set which is very costly and time-consuming to create if not impossible. Getting different detection technologies to interoperate effectively and efficiently has become a big challenge for building an operational hybrid intrusion detection system. 4. Results 3. Methodology a) The Proposed Hybrid Detector Structure