Analysis of Support Vector Machine-based Intrusion Detection Techniques

• Published in: Arabian journal for science and engineering (2011) 2020-04, Vol.45 (4), p.2371-2383
• Main Authors: Bhati, Bhoopesh Singh, Rai, C. S.
• Format: Article
• Language: English
• Subjects: Data collection; Data exchange; Datasets; Decision analysis; Distance learning; Engineering; Humanities and Social Sciences; Intrusion detection systems; multidisciplinary; Online banking; Research Article – Special Issue - Intelligent Computing and Interdisciplinary Applications; Science; Support vector machines
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-019-03970-z

From the last few decades, people do various transaction activities like air ticket reservation, online banking, distance learning, group discussion and so on using the internet. Due to explosive growth of information exchange and electronic commerce in the recent decade, there is a need to implement some security mechanisms in order to protect sensitive information. Detection of any intrusive behavior is one of the most important activity for protecting our data and assets. Various intrusion detection systems are incorporated in the network for detecting intrusive behavior. In this paper, an analytical study of support vector machine (SVM)-based intrusion detection techniques is presented. Here, the methodology involves four major steps, namely, data collection, preprocessing, SVM technique for training and testing and decision. The simulated results have been analyzed based on overall detection accuracy, Receiver Operating Characteristic and (ROC) Confusion Matrix. NSL-KDD dataset is used to analyze the performance of SVM techniques. NSL-KDD dataset is a benchmark for intrusion detection technique and contains huge amount of network records. The analyzed results show that Linear SVM, Quadratic SVM, Fine Gaussian SVM and Medium Gaussian SVM give 96.1%, 98.6%, 98.7% and 98.5% overall detection accuracy, respectively.