Journal Article

Intelligent IDS in wireless sensor networks using deep fuzzy convolutional neural network

Selvi, M., Subramani, Shalini

2023

Overview

The intrusion detection systems (IDSs) developed based on classification algorithms for securing wireless sensor networks (WSNs) are unable to attain the required detection accuracy. To handle the security issue in WSN, an intelligent IDS is proposed in this work by using a convolution neural network (CNN)-based deep learning approach along with a fuzzy inference model. The proposed IDS keeps track of the network and system activities by using the proposed fuzzy CNN along with spatial and temporal constraints to detect malicious nodes. Moreover, this algorithm has been modelled mathematically by using Feynman Path Integral and Schrodinger equation for handling the spatial and temporal constraints with fuzzy rules. From the experiments conducted in this work, it is proved that the proposed IDS increases the security, detection accuracy and packet delivery ratio, but decreases the delay and false positive rate in WSNs when compared with the existing IDSs.

• Publisher: Springer London,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial Intelligence / Artificial neural networks / Classification / Communication / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Datasets / Deep learning / Feature selection / Fuzzy logic / Image Processing and Computer Vision / Intrusion detection systems / Machine learning / Neural networks / Original Article / Probability and Statistics in Computer Science / Schrodinger equation / Security systems / Sensors / Wireless sensor networks