Citation

Subbiah, Sridevi and Sonai Muthu Anbananthen, Kalaiarasi and Thangaraj, Saranya and Kannan, Subarmaniam and Chelliah, Deisy (2022) Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm. Journal of Communications and Networks, 24 (2). pp. 264-273. ISSN 1229-2370

Full text not available from this repository.

Abstract

Attacks in wireless sensor networks (WSNs) aim to prevent or eradicate the network's ability to perform its anticipated functions. Intrusion detection is a defense used in wireless sensor networks that can detect unknown attacks. Due to the incredible development in computer-related applications and massive Internet usage, it is indispensable to provide host and network security. The development of hacking technology tries to compromise computer security through intrusion. Intrusion detection system (IDS) was employed with the help of machine learning (ML) Algorithms to detect intrusions in the network. Classic ML algorithms like support vector machine (SVM), K-nearest neighbour (KNN), and filter-based feature selection often led to poor accuracy and misclassification of intrusions. This article proposes a novel framework for IDS that can be enabled by Boruta feature selection with grid search random forest (BFS-GSRF) algorithm to overcome these issues. The performance of BFS-GSRF is compared with ML algorithms like linear discriminant analysis (LDA) and classification and regression tree (CART) etc. The proposed work was implemented and tested on network security laboratory — knowledge on discovery dataset (NSL-KDD). The experimental results show that the proposed model BFS-GSRF yields higher accuracy (i.e., 99%) in detecting attacks, and it is superior to LDA, CART, and other existing algorithms.

Item Type:	Article
Uncontrolled Keywords:	Wireless sensor networks, Support vector machines, Machine learning algorithms
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Jul 2022 02:13
Last Modified:	05 Jul 2022 02:13
URII:	http://shdl.mmu.edu.my/id/eprint/10139

Downloads

Edit (login required)