Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System
Malathi Eswaran1, P. Balasubramanie2, M. Jotheeswari3

1Malathi Eswaran, Department Computer Technology – PG, Kongu Engineering College, Erode.
2P. Balasubramanie, Department of Computer Science & Engineering Kongu Engineering College, Erode.
3M. Jotheeswari, Department Computer Technology – PG, Kongu Engineering College, Erode.

Manuscript received on October 14, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 1152-1156 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4443119119/2019©BEIESP | DOI: 10.35940/ijitee.A4443.119119
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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: Web use and digitized information are getting expanded each day. The measure of information created is likewise getting expanded. On the opposite side, the security assaults cause numerous security dangers in the system, sites and Internet. Interruption discovery in a fast system is extremely a hard undertaking. The Hadoop Implementation is utilized to address the previously mentioned test that is distinguishing interruption in a major information condition at constant. To characterize the strange bundle stream, AI methodologies are used. Innocent Bayes does grouping by a vector of highlight esteems produced using some limited set. Choice Tree is another Machine Learning classifier which is likewise an administered learning model. Choice tree is the stream diagram like tree structure. J48 and Naïve Bayes Algorithm are actualized in Hadoop MapReduce Framework for parallel preparing by utilizing the KDDCup Data Corrected Benchmark dataset records. The outcome acquired is 89.9% True Positive rate and 0.04% False Positive rate for Naive Bayes Algorithm and 98.06% True Positive rate and 0.001% False Positive rate for Decision Tree Algorithm.
Keywords: Big Data, Decision Tree, Hadoop Framework, Intrusion Detection, Machine Learning, Naïve Bayes.
Scope of the Article: Big Data