Network Intrusion Classification Employing Machine Learning: A Survey
Akhi, Afroja Bhuiyan
Kanon, Esrat Jahan
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In this modern era computer network security is a vital issue. Network security is developed by an efficient Intrusion Detection System (IDS). It is used to identify unauthorized access, malicious attacks and give an alert when monitors any kind of unusual activity. Over the past 30 years, there have been lots of work on intrusion detection system using machine learning algorithms. Basically, realizing the present status of application of machine learning algorithms for solving intrusion classification task, this review work gives a proper guideline. This survey work selected 84 papers based on highest citations number from the years of 2009-2018. This thesis work gives an overview of a different intrusion detection systems, a statistical comparison based on different classifier like single, hybrid and ensemble learning. In addition, we have discussed best machine learning classifiers, best datasets and some feature selections process in this thesis work.
- B.Sc Thesis/Project