Analysis of Intrusion Detection and Classification using Machine Learning Approaches
Keywords:Intrusion Detection System, Anomaly Detection, Supervised learning, Unsupervised, Detection Rate.
As the network primarily based applications are growing quickly, the network security mechanisms need a lot of attention to enhance speed and preciseness. The ever evolving new intrusion types cause a significant threat to network security. Though varied network security tools are developed, however the quick growth of intrusive activities continues to be a significant issue. Intrusion detection systems (IDSs) are wont to detect intrusive activities on the network. Analysis showed that application of machine learning techniques in intrusion detection might reach high detection rate. Machine learning and classification algorithms facilitate to design “Intrusion Detection Models” which might classify the network traffic into intrusive or traditional traffic. This paper discusses some usually used machine learning techniques in Intrusion Detection System and conjointly reviews a number of the prevailing machine learning IDS proposed by researchers at different times. in this paper an experimental analysis is performed to demonstrate the performance analysis of some existing techniques in order that they will be used further in developing Hybrid Classifier for real data packets classification. The given result analysis shows that KNN, RF and SVM performs best for NSL-KDD dataset.
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Copyright (c) 2017 Anjum Khan, Anjana Nigam
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