Affiliations: [a] Department of Computer Science and Engineering, Kalasalingam University, Tamilnadu, India
| [b] Department of Electrical and Electronics Engineering, Kalasalingam University, Tamilnadu, India
| [c] Department of Electronics and Instrumentation Engineering, Kalasalingam University, Tamilnadu, India
Correspondence:
[*]
Corresponding author. R. Vijayanand, Department of Computer Science and Engineering, Kalasalingam University, Tamilnadu, India. E-mail: rkvijayanand@gmail.com.
Abstract: Intrusion detection is an important requirement in wireless mesh network and the intrusion detection system (IDS) provides security by monitoring data traffic in real time. This work proposes support vector machine (SVM) classifier to identify the intrusion in the network. The traffic data collected from the wireless mesh network (WMN) is given as input to the SVM. The irrelevant and redundant input variables increase the complexity of designing IDS and may degrade its performance. Hence, feature selection techniques, which select the relevant features from the original input is essential to improve the performance of IDS in WMN. In this work, a hybrid genetic algorithm (GA) and mutual information (MI) based feature selection technique is proposed for IDS. The performance of IDS with the proposed feature selection technique is analyzed with IDS having mutual information, genetic algorithm and GA+MI based feature selection techniques using ADFA-LD dataset. Experimental results have demonstrated the effectiveness of proposed intrusion detection system with hybrid feature selection technique in wireless mesh network. The superiority of SVM classifier with hybrid feature selection technique is also verified by comparing with artificial neural network classifier.
Keywords: IDS for WMN, SVM based IDS, ADFA-LD dataset, GA with MI technique, Hybrid feature selection
