Network Intrusion Detection System using K-Means Clustering and Gradient Boosted Tree Classifier
Nandini Rebello1, Manamohan K2
1Nandini Rebello, Department of Computer Science & Engineering, Manipal Institute of Technology, MAHE, India.
2Manamohan K, Department of Computer Science & Engineering, Manipal Institute of Technology, MAHE, India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 22 June 2019 | PP: 866-869 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C11840283S19/19©BEIESP
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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 intrusion detection is an important and dynamic research area because the internet is always subjected to an ever increasing number of security threats. As the type of attacks appearing is continuously changing, there is a need for developing adaptive and flexible security features. This is where anomaly-based network intrusion detection techniques are important to protect the network against malicious activities. In literature, many such intrusion detection systems have been proposed till date. In this paper, a hybrid model for intrusion detection by performing K-means clustering to form cluster models of the dataset and input it to the Gradient Boosted Tree classifier has been proposed. In order to evaluate the performance metrics the NSL-KDD dataset was used. The proposed model showed improved results having high detection rate of 99.3% and low false alarm rate of 0.19%.
Keywords: Anomaly Detection, K-Means Clustering, Gradient Boosted Tree Classification, Intrusion Detection.
Scope of the Article: Clustering