Blocking intrusion logic using optimized multi-head convolution in wireless sensor network

Title

Blocking intrusion logic using optimized multi-head convolution in wireless sensor network

Creator

Prabhu S.; Mary Anita E.A.; Mohanageetha D.

Description

Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively. 2023 - IOS Press. All rights reserved.

Source

Journal of Intelligent and Fuzzy Systems, Vol-45, No. 4, pp. 6897-6909.

Date

2023-01-01

Publisher

IOS Press BV

Subject

k-means clustering; multi-head convolutional neural network; Salp Swarm optimization; Wireless sensor nodes

Coverage

Prabhu S., Department of Computer Science and Engineering, S.A. Engineering College, Tamil Nadu, Chennai, India; Mary Anita E.A., Department of Computer Science Engineering, Christ University, Karnataka, Bengaluru, India; Mohanageetha D., Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India

Rights

Restricted Access

Relation

ISSN: 10641246

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3233/JIFS-231756

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173900285&doi=10.3233%2fJIFS-231756&partnerID=40&md5=fbd90c183cd9f237a7ab803adbd95cba