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Volume & Issue no: Volume 8, Issue 11, November 2019

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Title:
An Efficient way of Detecting Intrusions in Cloud using Naïve Bayes, J48 and Random Forest Classifier Algorithms
Author Name:
N. P. Ponnuviji , M. Vigilson Prem
Abstract:
ABSTRACT Cloud computing has become a wide area of using different resources in a variety of applications in a more cost effective manner. There are at present, a nominal amount of service providers and service brokers willing to share and offer required resources across any part of the globe. As things have become flexible, so are the threats in the cloud computing. Accessing the resources in a more secured manner is still considered as a challenge in the cloud computing arena. With the advent of various Intrusion Detection and Prevention tools being used to monitor and notify the attacks, the anomaly intrusion still poses a problem in the cloud computing. The anomaly intrusion reflects the change in the behaviour of the network by identifying the rare events occurring in the Virtual Machine of the cloud. The unknown data depicts the rare events, where the Intrusion Detection System (IDS) detects the data by using the different anomaly classification algorithms, analyse the reason for the events and send the detailed report to the administrator of the cloud. This paper proposes the use of Random Forest, Naïve Bayes and Decision Tree (J48) classification algorithms for capturing the malicious data. With the help of these algorithms, the huge false alarm rates can be overcome. The proposed work has been implemented by using the WEKA tool to generate a detailed statistical report based on the calculation time and performance of each algorithm using the NSL KDD dataset. Keywords: Virtual Machine (VM), Intrusion Detection System (IDS), Random Forest, Naïve Bayes, Decision Tree (J48), NSL KDD dataset.
Cite this article:
N. P. Ponnuviji , M. Vigilson Prem , " An Efficient way of Detecting Intrusions in Cloud using Naïve Bayes, J48 and Random Forest Classifier Algorithms" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 8, Issue 11, November 2019 , pp. 024-032 , ISSN 2319 - 4847.
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