Intrusion Detection System Based on Immune Algorithm and Support Vector Machine in Wireless Sensor Network

2011 ◽  
pp. 372-376 ◽  
Author(s):  
Yu Sheng Chen ◽  
Yu Sheng Qin ◽  
Yu Gui Xiang ◽  
Jing Xi Zhong ◽  
Xu Long Jiao
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Immune Algorithm ◽  
Wireless Sensor ◽  
Support Vector

scholarly journals A Review of Support Vector Machine-based Intrusion Detection System for Wireless Sensor Network with Different Kernel Functions

2021 ◽  
Vol 11 (1) ◽  
pp. 59-67
Author(s):  
Muhammad Amir Hamzah ◽  
Siti Hajar Othman
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Kernel Function ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Support Vector

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 intrusion detection system based on MK-ELM

2020 ◽  
Vol 24 (16) ◽  
pp. 12361-12374 ◽  
Author(s):  
Wenjie Zhang ◽  
Dezhi Han ◽  
Kuan-Ching Li ◽  
Francisco Isidro Massetto
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System

scholarly journals HTBIDS: Hybrid Trust Based Intrusion Detection System for Physical Layer in WSN

2021 ◽  
Vol 10 (1) ◽  
pp. 49-53
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Physical Layer ◽  
Wireless Sensor ◽  
Network Communication ◽  
Detection Accuracy ◽  
Jamming Attack

In recent years, wireless sensor network (WSN) is the measure concern over network communication. A number of attacks are occurred at the time of network communication as result it hampers the smooth functionality, data flow and data transmission. In this article, we have proposed a trust-based intrusion detection system for physical layers attacks using DRI and Cross Check method. The HTBIDS is effective method to identify the abnormal nodes in wireless sensor network. The abnormal nodes are attacked by periodic jamming attack. We have considered the periodic jamming attack at physical layer for performance evaluation. Results show that HTBIDS performs better using detection accuracy (DA) and false alarm rate (FAR).

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