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Title: Hybrid intrusion prediction system based on Fuzzy Colored Petri-Net

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Title:
Hybrid intrusion prediction system based on Fuzzy Colored Petri-Net
Author Name:
Ghodhbani Salah and Jemili Farah
Abstract:
Intrusion detection systems (IDS) play a very important role in minimizing the damage caused by different computer attacks. However they cannot detect all attacks, and they are not capable of predicting future attacks. In this paper, we propose a novel approach of hybrid intrusion prediction system (IPS) that can not only detect attacks but also predict future probable intrusion. The proposed system model intruders actions against internal machines which provide a global view to network administrators and warns them about future probable intrusions and reduces the damage caused due to attacks. Future attacks are predicted based on intruder’s behavior clusters detected in an earlier phase of clustering. In this paper, we present the architecture, and the implementation of the proposed system. Our experiments on real world datasets show that the proposed system is efficient with high prediction rate.
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