A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks

Wireless Internet of Things (IoT) Sensor Networks (WIoTSNs) are frequently deployed in resource-constrained environments where security threats pose significant challenges. Existing intrusion detection systems (c) often struggle with scalability and efficiency under the unique demands of IoT networks. This work introduces an Intrusion Detection System (IDS) framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks in a hybrid architecture, enhanced by an attention mechanism to improve feature extraction and classification accuracy. To address computational demands, an enhanced Particle Swarm Optimization (PSO) algorithm is implemented for dynamic feature selection, thereby optimizing the system's efficiency in high-dimensional data environments characteristic of IoT networks. The proposed model enhances IoT intrusion detection by integrating a novel hybrid CNN-LSTM with an attention mechanism, thereby improving feature extraction and temporal pattern recognition. Additionally, the improved dynamic PSO algorithm optimizes feature selection in real time, enhancing classification accuracy and adaptability to evolving IoT network threats. This combination ensures more efficient and robust intrusion detection in dynamic IoT environments. Experimental evaluations using a standard IoT intrusion dataset indicate that the proposed model achieves notable accuracy rates of 98.73% with CNN, 99.87% with LSTM, 99.12% with CNN-LSTM, and 98.88% with the enhanced CNN-LSTM with attention, demonstrating an improvement over existing techniques. The framework's resilience and adaptability underscore its potential for enhancing network security in real-world IoT applications by addressing evolving threats and computational constraints.