IoTCommFreq: Efficient IoT Device Fingerprinting Through Network Behaviour Analysis Using Frequency of Communication

The rapid growth of Internet of Things (IoT) devices increases the need for robust and efficient device identification techniques to safeguard network security and integrity. Device fingerprinting emerges as a promising approach for identifying IoT devices; however, existing methods require substantial computational and memory resources, which limits their suitability for real-time and resource-constrained environments. This study presents a novel IoT device fingerprinting technique based on the frequency of communication, leveraging the inherent communication patterns of IoT devices. The proposed technique is computationally lightweight, resource-efficient, and adaptable to dynamic network environments. To enhance transparency and trust, this work incorporates explainable machine learning techniques by leveraging feature importance scores to improve the interpretability of classification decisions and reveal the contribution of communication frequency features to model predictions. Experimental evaluation using multiple benchmark datasets shows that the proposed technique has excellent performance, achieving an accuracy of up to 99.9% and demonstrating minimal inference time (as low as 0.001 seconds). It significantly outperforms existing methods by reducing computational overhead and offers a practical and scalable solution for real-world IoT deployments.