A Novel Cloud Intrusion Detection System Using Feature Selection and Classification

Citations Over TimeTop 10% of 2015 papers

Abstract

This paper proposes a new cloud intrusion detection system for detecting the intruders in a traditional hybrid virtualized, cloud environment. The paper introduces an effective feature selection algorithm called Temporal Constraint based on Feature Selection algorithm and also proposes a classification algorithm called hybrid decision tree. This hybrid decision tree has been developed by extending the Enhanced C4.5 algorithm an existing decision tree based classifier. Furthermore, the experiments conducted on the sample Cloud Intrusion Detection Datasets (CIDD) show that the proposed cloud intrusion detection system provides better detection accuracy than the existing work and reduces the false positive rate.

Related Papers

• → Machine Learning Applications in Misuse and Anomaly Detection(2020)23 cited
• → Innovative Genetic Approach for Intrusion Detection by Using Decision Tree(2013)8 cited
• → Intrusion Detection Techniques(2004)70 cited
• → Soft Computing‐Based Intrusion Detection System With Reduced False Positive Rate(2020)3 cited
• Review on Data Mining Techniques forIntrusion Detection System(2014)