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JournalInternational Journal of Computer Applications
TitleReduction of False Alarm Rate by using K-NN and Naive Bayes: A Review
Index TermAutomated Systems
AbstractInterruption location is basic in orchestrate security. Most present framework interruption location structures (NIDSs) employ either misuse recognition or anomaly discovery. In any case, misuse recognition can't recognize darken interruptions, and anomaly location generally has high false positive rate. To overcome the imperatives of the two techniques, they intertwine both anomaly and misuse recognition into the NIDS. This paper presents a hybrid interruption recognition framework based on the combination of k-Means and two classifiers which are K-nearest neighbor and Naive Bayes. This paper includes picking features using an entropy based segment assurance computation that uses imperative properties and expels the irredundant qualities. The whole observation in this study is performed on KDD-99 Data set which is accepted at world level for surveying execution of various interruption recognition frameworks. The consequent stage is grouping stage using k-Means. The proposed framework can recognize all interruptions and categorize them into four segments: Denial of Service, User to Root, Remote to nearby and test. The main goal is to minimize the false ready rate of IDS.
KeywordsKDD, NIDS, DoS, R2L, U2R, DR, FPR.
No. of Pages4
Author NamesNavita Datta, Rajeev Kumar, Reeta Bhardwaj
Author Emailsernavitasharma@gmail.com, rajeev.daviet@gmail.com, er.reeta@gmail.com
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Volume180
Issue3
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