Intrusion Detection System based on K-means, Classification and Regression Trees Algorithm

The security of computer networks has become a very important aspect intoday’s era. An intrusion detection system (IDS) is a type of security softwaredesigned to automatically inform administrators when someone is trying tocompromise the information system through malicious activities or securitypolicy violations. IDS works by monitoring the system functionalities bychecking system vulnerabilities, the integrity of files, and performing analysis ofpatterns based on known attacks. Intrusion detection is the cycle calleddistinguishing intrusions. The activity which is entering a framework withoutconsent is called interruption. Interruption Detection Systems are fundamentalfor security limits. This paper is focused on the analysis of five differenttechniques like k-means clustering and naive Bayes classification, Enhanced kmeans algorithm, random forest, and weighted k-means, k-means clustering,regression trees algorithm, etc. But some problems are indicated by techniques.These methods analyzed the restrictions of intrusion detection systems by wayof low accuracy, high incorrect alarm rate, and time consumption. So, toovercome these problems the proposed method ‘K-means and Classification andRegression Trees Algorithm’ is used to show good accuracy in performanceanalysis with time complexity by using a hybrid data mining method in theWeka tool. The proposed model of K-means and classification and regressiontrees algorithm depends on intrusion detection system that gives proper periodpopular huge information measure of these days’ interruption data set.