Feature Selection Based Supervised Learning Method for Network Intrusion Detection
Ch. Mallikarjuna Rao1, G. Ramesh2, D. V. Lalitha Parameswari3 Karanam Madhavi4, K. Sudheer Babu5
1Dr. Ch. Mallikarjuna Rao, Professor, Department of Computer Science Engineering, GRIET, Hyderabad, Telangana, India.
2Dr. G. Ramesh, Associate Professor, Department of Computer Science Engineering, GRIET, Hyderabad, Telangana, India.
3Dr. D. V. Lalitha Parameswari, Sr.Asst. Professor, Department of Computer Science Engineering, GNITS, Hyderabad, Telangana, India.
4Dr. Karanam Madhavi, Professor, Department of Computer Science Engineering, GRIET, Hyderabad, Telangana, India.
5Mr. K. Sudheer Babu, Assistant Professor, Department of Computer Science Engineering, GRIET, Hyderabad, Telangana, India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 24 May 2019 | Manuscript published on 30 May 2019 | PP: 2796-2802 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1275058119/19©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: Supervised learning is one of the data mining phenomena where a knowledge model is built for artificial intelligence. Learning from training samples has its advantages in predictive solutions. Such solution is essential for network intrusion detection problems. Networks of all kinds do have problem of intrusions as they are exposed to public communications in one way or other. Intrusions over a network are in the form of network flows that need to be analyzed. Manual observation of the flows and detecting intrusions is very time taking. Therefore it is essential to have an automated system for quickly detection of intrusions to safeguard network systems. There are many intrusion detection systems found in the literature. However, there is need for faster algorithm that makes sense in helping network administrators with accurate knowledge presented. Towards this end we proposed a framework with a feature subset selection mechanism to speed up detection process and improve accuracy of the same. The feature subset selection algorithm and Support Vector Machine (SVM) work together in order to have a faster detection system. Benchmark datasets like KDD and NSL-KDD are used for experiments. The empirical results showed that the proposed SVM-FSS framework shows better performance over the state of the art framework.
Index Terms: Data Mining, Feature Selection, Intrusion Detection, Support Vector Machine, Machine Learning.
Scope of the Article: Machine Learning