Systematic Literature Survey on IDS Based on Data Mining

Abstract

In this digital era, the usage of internet and information grows rapidly. Every fraction of second, huge volume of data is transferred from one network to another. This information and information system are subjected to attack. It is necessary to protect this valuable information and network from intruders generally named as crackers or hackers who are threat to system security. System security is a common, current and critical problem which is a challengeable task to researchers. Intrusion Detection System (IDS) offers good solution to this problem. With aim of boost up the performance of IDS, it is integrated with data mining. Various data mining techniques in IDS, based on certain metrics like accuracy, false alarm rate, detection rate and issues of IDS have been analyzed in this paper. A total of 43 papers were reviewed in the period 2008 to 2018. It is observed that more number of articles support SVM or ANN Techniques. Also it is observed that hybrid methods produce better performance than single. This survey shows that in hybrid methods, frequently K-means or SVM technique are combined with others and gives good result.