DNN-BASED INTRUSION DETECTION FOR ENHANCING LORAWAN SECURITY: CURRENT APPLICATIONS AND FUTURE PROSPECTS

Abstract

The rapid adoption of LoRaWAN technology in smart cities, industrial IoT, and critical infrastructure has amplified concerns over its vulnerability to sophisticated cyber-attacks. Existing security solutions often struggle to meet the accuracy, adaptability, and scalability requirements of resource-constrained, long-range communication systems. This paper proposes a Deep Neural Network (DNN)-based Intrusion Detection System (IDS) for Long Range Wide Area Network (LoRaWAN)-based smart communications. The model architecture comprises multiple fully connected layers with Rectified Linear Unit  (ReLU) activation for effective non-linear feature extraction. The Adam optimizer is employed to achieve accelerated convergence during training, and a Softmax output layer is used to perform multi-class classification across the different attack categories. The CICIDS2017 dataset, a comprehensive and realistic benchmark for network intrusion detection research, is used for performance evaluation. Experimental results reveal exceptional detection capability against complex cyber-attacks under a vast range of performance parameters. The proposed IDS have achieved 99.98% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1-score. These outcomes demonstrate the model’s robustness in differentiating legitimate LoRaWAN traffic from a wide spectrum of malicious activities in real time. Furthermore, the proposed approach exhibits high generalization potential, making it suitable for deployment in diverse Internet of Things (IoT)-based environments. Future work will focus on lightweight model optimization and real-world LoRaWAN traffic validation to ensure practical applicability in large-scale smart communication networks. Additionally, this research underscores the current applications along with the future prospects of secure LoRaWAN communications.