Dynamic trusted cross-layer IDS for secured communications in wireless networks using routing algorithm and FT-CNN

Abstract

Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy.