Optimized Ensemble Weak Learner Tree Based Network Efficient Intrusion Detection and Alert System using Data Mining

Abstract

The modern society accesses various network services through different devices. However, the services afford by the service provider faces various challenges and threats. The services are facing different network threats towards degrading the service performance or the entire network. Number of approaches discussed earlier to restrict the illegal access from malicious users which uses different properties in service level, packet level, user level features. However, they suffer to achieve higher performance in intrusion detection. To improve the performance in intrusion detection an novel tree based ensemble learner algorithm has been proposed in this paper. The method incorporates Random Forest and Random Trees, which are identified as NP complete. The method maintains the list of ensembles which are indexed under trees. At the classification, the Tabu Search algorithm has been used which measures the ensemble class weight (ECW) which has been used to perform classification. According to the result of intrusion detection, an alert has been generated to the administrator. The proposed algorithm improves the performance of intrusion detection.