Decision Tree: A Machine Learning for Intrusion Detection
Shilpashree. S1, S. C. Lingareddy2, Nayana G Bhat3, Sunil Kumar G4
1Shilpashree. S, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering Bengaluru, Karnataka, India.
2S. C. Lingareddy, Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering Bengaluru, Karnataka India.
3Nayana G Bhat, Department of Computational Engineering, Centre For Incubation, Innovation, Research and Consultancy, Jyothy Institute of Technology Bengaluru, Karnataka India.
4Sunil Kumar G, Department of Computer Science and Engineering, Vijaya Vittala Institute of Technology, Bengaluru, Karnataka India.
Manuscript received on 13 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript Published on 26 July 2019 | PP: 1126-1130 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12340486S419/19©BEIESP | DOI: 10.35940/ijitee.F1234.0486S419
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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: The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.
Keywords: Intrusion Detection System, Machine Learning, Deep Learning, Decision Tree.
Scope of the Article: Machine Learning