Authors: Kapil K. Wankhade; Kalpana C. Jondhale
Addresses: Research Scholar, Swami Ramanand Teerth Marathwada University, Nanded and SGGS Institute of Engineering and Technology, Nanded, MS, 431606, India ' Department of Electronics Engineering, MGM College of Engineering, Nanded, MS, India
Abstract: The amount of data in the field of computer networking is growing rapidly and this urges new challenges in the field of an intrusion detection system (IDS). To handle such increasing volume of data, a new hybrid approach has to be developed to overcome the problems such as high detection rate and low false alarm rate. An intrusion detection system plays a vital role in the detection of malicious attacks. Data mining and machine learning techniques are important and play a vital role in the detection of attacks. This paper mainly focuses on detection rate and false alarm rate and so to resolve these problems a hybrid method, ensemble clustering, has been proposed. This method tries to increase detection rate with lowering false alarm rate. The method has been tested on KDDCup'99 network intrusion dataset and performs well as compared with other algorithms in terms of detection rate and false alarm rate.
Keywords: boosting; classification; clustering; data mining; divide and merge; detection rate; false alarm rate; intrusion detection system; IDS; ensemble method; k-means.
International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.2/3, pp.112 - 140
Received: 13 Jan 2017
Accepted: 15 Sep 2017
Published online: 05 Apr 2019 *