Intrusion Detection System using Datamining Based Enhanced Framework
S. Suganthi Devi
S.Suganthi Devi, lecturer in Srinivasa Subbaraya Polytechnic College, Puthur, Nagappatinam, Tamil Nadu
Manuscript received on October 14, 2019. | Revised Manuscript received on 19 October, 2019. | Manuscript published on November 10, 2019. | PP: 5013-5017 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4472119119/2019©BEIESP | DOI: 10.35940/ijitee.A4472.119119
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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: With the significant increase in the use of computers over the network and the development of applications on different platforms, the focus is on network security. The identification of multiple attacks is actually an important element of network security. The role of the IDS is to track and prevent unauthorized use or damage to network resources and systems. An intrusion detection system using Datamining Based Enhanced Framework (DEF) is presented in this paper. The model is assisted by the K-mean Clustering and Decision Tree (DT) classification techniques in which genetic algorithms (GA) for clusters, max runs and confidence can be used. The experimental results shows the promising outcome of the proposed Datamining Based Enhanced Framework (DEF).
Keywords: Datamining, Network Security, Genetic Algorithms, Intrusion Detection System (IDS)
Scope of the Article: Patterns and Frameworks