Ensemble approach for network threat detection and classification on cloud computing

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Abstract

Summary As Network traffic rises and attacks become more widespread and complicated, we must come across Innovative ways to enrich Intrusion Detection Systems in Cloud Computing. This paper proposes the Ensemble approaches for Network Intrusion Detection and Classification in Cloud. The major aids of the Ensemble Learning to improve the outcome of each Machine Learning Algorithms and to get a robust Classifier. Real Time Malicious Network Streams Samples were collected using Honeynet, which is deployed on cloud environment. We use supervised learning and Unsupervised learning algorithms for classifying the known malicious network streams and unknown malicious streams. Network related attacks can be segregated into four classes, namely, Denial of service (DOS), User to root (U2 R), Remote to local (R2L), and probe, and the vital constraints that must be overcome with the end goal to build efficient Intelligent Intrusion Detection. The motivation behind the proposed work is to enhance the accuracy rate with response time. The outcome obtained from the Ensemble method has better accuracy rate compared to the SVM, Naive Bayes, and Logistic regression method.

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