Machine Learning and Feature Selection Approach for Anomaly based Intrusion Detection: A Systematic Novice Approach
Amrita1, Shri Kant2
1Amrita, Department of Computer Science and Engineering, Symbiosis Entrance Test, Sharda University, Greater Noida, Uttar Pradesh, India.
2Shri Kant, Department of Computer Science and Engineering, Symbiosis Entrance Test, Sharda University, Greater Noida, Uttar Pradesh, India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 April 2019 | PP: 434-443 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60930486S19/19©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 System (NIDS) has become an imminent research area in network and information security due to the proliferation of the Internet and rapid increase in anomalous activities or intrusions. NIDS helps to detect anomalous activities or intrusions which compromise CIA (confidentiality, integrity, and availability), violate the security policies and mechanisms of a computer network. This paper presents a survey on anomaly based NIDS using machine learning technique employing feature selection approach. The prime contribution of this research is to present technical and empirical evaluation of each paper. The state-of-the-art NIDS is systematically analyzed and discussed according to machine learning and feature selection techniques used, number of selected features, efficiency in terms of various performance metrics and its result. This paper also provides an idea of selecting more appropriate solution and also the scope of improvement for each specific case.
Keywords: Anomaly Detection, Machine Learning, Network Intrusion Detection System, Feature selection.
Scope of the Article: Machine Learning