An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection
D. Vivek, K1, Selvanayaki2, C. AnoorSelvi3
1Dr. D. Vivek, Sri Krishna College of Engineering and Technology, Coimbatore.
2Dr. K. Selvanayaki, Tamilnadu College of Engineering, Coimbatore.
3C. Anoor Selvi, VSB College of Engineering, Karur.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 219-222 | Volume-8 Issue-9, July 2019 | Retrieval Number: H6821068819/19©BEIESP | DOI: 10.35940/ijitee.H6821.078919
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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: Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques. I
Keywords: Performed, Learning Evolution Machines Application Clustering Application
Scope of the Article: Deep Learning