A Fuzzy Colored Petri-Net Approach for Hybrid Intrusion Prediction

Reducing the impact of computer attacks is crucial, and Intrusion Detection Systems (IDS) are an important tool in achieving this goal. However, IDSs have limitations and are unable to detect all attacks or anticipate future ones. To address this issue, we propose a new approach called a hybrid intrusion prediction system (IPS) that not only detects attacks but also predicts potential intrusions. By simulating the behavior of intruders on internal machines, our system provides network administrators with a comprehensive overview, enabling them to identify possible future intrusions and minimize the impact of attacks. Our study aims to predict future attacks based on the behavioral patterns of previously detected intrusions. We describe the architecture and implementation of our proposed system in this paper. Our experiments using real-world datasets demonstrate that the system is highly effective, achieving a high rate of accurate predictions.