An Intelligent Multi-Head Attention-Driven Temporal Convolutional Architecture for Intrusion Detection in Wireless Sensor Networks
Received: 23 September 2025 | Revised: 22 October 2025 | Accepted: 27 October 2025 | Online: 10 December 2025
Corresponding author: M. Pradeepa
Abstract
Wireless Sensor Networks (WSNs) have diverse uses but are prone to attacks due to their open deployment and low-cost devices. Preventive mechanisms are used to protect WSNs against certain types of attacks. Intrusion Detection Systems (IDSs) are essential as they prevent intruders from causing damage to the network. An IDS can gather information related to attack methods, helping prevent further damage. Deep Learning (DL) methods are widely applied in IDSs because they offer superior performance while processing uneven attacks in WSNs. This study presents an Intrusion Detection Framework for Securing WSNs using a Deep Representation Learning (IDFSWSN-DRL) model, aiming to develop an effective IDS to ensure real-time detection and mitigation of malicious activities. The data preprocessing stage applies robust scaling, label encoding, and data splitting to enhance data quality and support improved model accuracy. The Gazelle Optimization Algorithm (GOA) is employed for Feature Selection (FS). A Temporal Convolutional Network with Multi-Head Attention (TCN-MHA) is used for classification. Finally, the Catch Fish optimization Algorithm (CFA) is used for the hyperparameter tuning process. A comparison study of the IDFSWSN-DRL method illustrated a greater accuracy (99.69%) over recent models on the WSN-DS dataset.
Keywords:
wireless sensor networks, multi-head attention, intrusion detection, deep representation learning, attack, feature reductionDownloads
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