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A Survey on SVM Classifiers for Intrusion Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 98 - Number 19 |
| Year of Publication: 2014 |
| Authors: R. Ravinder Reddy, B. Kavya, Y Ramadevi |
10.5120/17294-7779
|
R. Ravinder Reddy, B. Kavya, Y Ramadevi . A Survey on SVM Classifiers for Intrusion Detection. International Journal of Computer Applications. 98, 19 ( July 2014), 34-44. DOI=10.5120/17294-7779
Abstract
Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. An Intrusion Detection is an important in assuring security of network and its different resourses. Intrusion detection attempts to detect computer attacks by examining various data records observed in processes on the network. Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection. Intrusion detection systems aim to identify attacks with a high detection rate and a low false positive. Here, we are going to propose Intrusion Detection System using data mining technique: Support Vector Machine (SVM). Support vector machine-based intrusion detection methods are increasingly being researched because it can detect novel attacks. But solving a support vector machine problem is a typical quadratic optimization problem, which is influenced by the feature dimensions and number of training samples. In this paper how the support vector machines are used for intrusion detection are described and finally proposed a solution to the inrusion detection system.
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