The week's pick
Reseach Article
Network Intrusion Detection using Semi Supervised Support Vector Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 85 - Number 9 |
| Year of Publication: 2014 |
| Authors: Jyoti Haweliya, Bhawna Nigam |
10.5120/14870-3245
|
Jyoti Haweliya, Bhawna Nigam . Network Intrusion Detection using Semi Supervised Support Vector Machine. International Journal of Computer Applications. 85, 9 ( January 2014), 27-31. DOI=10.5120/14870-3245
Abstract
The use of Internet is growing bit by bit and therefore huge amount of security threats faced in front of computer network system. Due to these threats secrecy of the information which is available in the network system is highly affected. To protect our network system from these threats, it becomes very important to build up a system that acts as a barrier between the network systems and the unessential security attacks. For the monitoring and detecting the intrusion (unwanted access), an Intrusion Detection Systems (IDS) were developed. But the expected performance and accuracy are not achieved by these systems. In this paper we propose a Semi Supervised Support Vector Machine (S3VM) to overcome these two concerns. The semi supervised SVM also overcomes the shortcoming of supervised SVM that require only labeled data for training the classifier. Semi Supervised Support Vector Machine is based on Self Training algorithm for semi supervised learning. The dataset used for training and testing purpose is NSL-KDD dataset. This model provides classification accuracy up to 90%.
References
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