Wireless sensor network (WSN) is among the popular communication technology which capable of self-configured and infrastructure-less wireless networks to monitor physical or environmental conditions. WSN also is the most standard services employed in commercial and industrial applications, because of its technical development in a processor, communication, and low-power usage of embedded computing devices. However, WSN is vulnerable due to the dynamic nature of wireless network. One of the best solutions to mitigate the risk is implementing Intrusion Detection System (IDS) to the network. Numerous researches were done to improve the efficiency of WSN-IDS because attacks in networks has been evolved due to the rapid growth of technology. Support Vector Machine (SVM) is one of the best algorithms for the enhancement of WSN-IDS. Nevertheless, the efficiency of classification in SVM is based on the kernel function used. Since dynamic environment of WSN consist of nonlinear data, linear classification of SVM has limitations in maximizing its margin during the classification. It is important to have the best kernel in classifying nonlinear data as the main goal of SVM to maximize the margin in the feature space during classification. In this research, kernel function of SVM such as Linear, RBF, Polynomial and Sigmoid were used separately in data classification. In addition, a modified version of KDD’99, NSL-KDD was used for the experiment of this research. Performance evaluation was made based on the experimental result obtained. Finally, this research found out that RBF kernel provides the best classification result with 91% accuracy.

Wireless sensor network is very popular in the industrial application due to its characteristics of infrastructure-less wireless network and self-configured for physical and environmental conditions monitoring. However, the dynamic environments of wireless network expose WSN to network vulnerabilities. Intrusion Detection System (IDS) has been used to mitigate the vulnerability issue of network. Researches towards the efficiency improvement of WSN-IDS has been extensively done because the rapid growth of technologies influence the growth of network attacks. Implementation Support Vector Machine (SVM) was found to be one of the optimum algorithms for the improvement of WSN-IDS. Yet, classification efficiency of SVM is based on the kernel function used because different kernel gives different SVM architecture. Linear classification of SVM has limitation to maximize the margin due to the dynamic environment of wireless network which consist of nonlinear data. Since maximizing the margin is the primary goal of SVM, it is crucial to implement the optimum kernel in the classification of nonlinear data. Each SVM model in this research use different kernels which are Linear, RBF, Polynomial and Sigmoid kernels. Further, NSL-KDD dataset was used for the experiment of this research. Performance of each kernel were evaluated based on the experimental result obtained and it was found that RBF kernel provides the best classification accuracy with the score of 91%. Finally, discussion based on the findings was made.