GMAC-Enhanced Secure IoT Communication with CNN-LSTM Hybrid Model for Intrusion Detection

This paper presents a hybrid CNN-LSTM model integrated with GMAC for intrusion detection and secure communication in IoT networks. The system uses Yule-Simon Distribution-Based Lyrebird Optimization Algorithm for feature selection that can achieve the highest anomaly detection accuracy at minimal computational cost in real-time applications. The proposed model uses GMAC with simultaneous encryption and authentication and the CNN-LSTM hybrid for the detection of intrusion in IoT traffic data. Hence, the detection accuracy and the processing time is much better compared to the traditional methods. System detection accuracy at 99.10% was achieved with a latency of 980ms; thus, there is robust performance with minimal overhead of computation. This advanced approach improves the security of IoT and offers an effective and scalable solution for smart city and industrial IoT networks, providing high accuracy, low latency, and strong encryption.