Network Intrusion Detection and Measuring the Data Set Performance by Machine Learning Technique (MLT)
A.Abirami1, R.M. Bhavadharini2, N.B.Prakash3, G.R.Hemalakshmi4
1D.Jothi*,Department of ECE, R.M.K.Engineering College, Chennai, India.
2L.Priyanka, Project Associate, Cognizant Technology Solutions, Chennai, India.

Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11806-11809 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9197118419/2019©BEIESP | DOI: 10.35940/ijrte.D9197.118419

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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 (

Abstract: Intrusion Detection System (IDS) is the most mainstream approach to protect a computer network from different malicious activities to identify an intrusion. There have been a lot of attempts towards more exceptional performance specifically in IDSs which depends on Data Mining (DM) and Machine Learning Techniques (MLT). Though there is a destructive issue in that available assessment, DataSet (DS), called KDD DS, can’t reflect current network circumstances and the most recent attack situations. As far as we could know, there is no possible assessment DS. We present a novel evaluation DS in this paper, called Kyoto, based on the 5 years of actual traffic information, which derived from different sorts of honey pots. This Kyoto DS is utilized for testing and assessing distinctive MLT has examined in this work. The attention was on unprocessed measurements True +ve (TrPo), False +ve (FaPo), True – ve (TrNa), and False – ve (FaNa) to assess execution and to improve the identification rate of IDS.
Keywords: Machine learning, Intrusion Detection System, Network Security, Malicious User, Attacks.
Scope of the Article: Machine Learning.