Intrusion detection technique using Coarse Gaussian SVM. (1st January 2021)

• Record Type: Journal Article
• Authors: Bhati, Bhoopesh Singh; Rai, C.S.
• Is Part Of: International journal of grid and utility computing. Volume 12:Number 1(2021)
• Page Start: 27
• Page End: 32
• Publication Date: 2021-01-01
• Subjects: information security -- intrusion detection -- machine learning -- Coarse Gaussian SVM -- anomaly detection -- networks security; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

In the new era of internet technology, everybody is transferring data from place to place through the internet. As internet technology is improving, different types of attacks have also increased. To detect the attacks it is important to protect transmitted information. The role of Intrusion Detection System (IDS) is very imperative to detect various types of attacks. Although researchers have proposed numerous theories and methods in the area of IDS, the research in area of intrusion detection is still going on. In this paper, Coarse Gaussian Support Vector Machine (CGSVM) based intrusion detection technique is proposed. The proposed method has four major steps namely, Data Collection, Pre-processing and Studying data, Training and Testing using CGSVM, and Decisions. In implementation, KDDcup99 data sets are used as a benchmark and MATLAB programming environment is used. The results of the simulation are presented by Receiver Operating Characteristics (ROC) and Confusion Matrix. The proposed method achieved detection rates as high 99.99%, 99.95%, 99.53%, 99.19%, 90.57% for DOS, Normal, Probe, R 2 L, U 2 R respectively.