Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : R.Karthik, Dr.S.Veni, Dr.B.L.Shivakumar
|DOI : 10.14445/22315381/IJETT-V41P212|
R.Karthik, Dr.S.Veni, Dr.B.L.Shivakumar"Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System", International Journal of Engineering Trends and Technology (IJETT), V41(2),66-71 November 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
This research work aims in design and development of an improved extreme learning machine classifier for intrusion detection system. The proposed research work contributed a single layer neural network which is trained starting with hidden nodes to the maximum number of hidden nodes and the expected learning accuracy. The improved ELM makes use of an intermediate variable in the overall recursive process which obtains better learning rate with reduced error. KDD cup’99 dataset that contains four major types of attacks in the network is chosen for performing IELM classification. Performance metrics detection rate and false alarm rate are chosen. Simulation results shows that the proposed IELM classifier outperforms in terms of improved detection rate and reduced false alarm rate.
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IELM, KDD cup’99, IDS, DoS.