Optimized Ensemble Weak Learner Tree Based Network Efficient Intrusion Detection and Alert System using Data Mining
K. Mohanapriya1, M. Savitha Devi2

1K. Mohanapriya, Guest Lecturer, Department of Computer Science, Government Arts College for Women, Krishnagiri (Tamil Nadu), India.

2Dr. M. Savitha Devi, Assistant Professor, Department of Computer Science, Periyar University Constituent College of Arts & Science, Harur (Tamil Nadu), India.

Manuscript received on 12 January 2020 | Revised Manuscript received on 08 February 2020 | Manuscript Published on 20 February 2020 | PP: 415-420 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10890193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1089.0193S20

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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.

Keywords: Network Intrusion Detection Systems (NIDS), Decision Trees, Random Forest, Random Trees, Ensemble Weak Learner Tree and Tabu Search (TS).
Scope of the Article: Data Mining