IDENTIFY: Intelligent device identification using device fingerprints and machine learning
Liwei Liu, Muhammad Ajmal Azad, Harjinder Singh Lallie, Hany F. Atlam · Pervasive and Mobile Computing · 2025
The Internet of Things (IoT) consists of a rapidly growing network of heterogeneous devices that autonomously monitor, collect, and exchange data across a wide range of application domains. The rapid increase of IoT devices highlighted the importance of scalable, secure, and adaptive network management strategies for dynamic networks. A key challenge in this context is the automatic identification of devices, which is critical for detecting and mitigating malicious devices that can compromise network integrity. Accurate device identification strengthens the security of dynamic IoT environments by facilitating early detection of anomalous or adversarial traffic. Device fingerprinting offers a non-intrusive solution by leveraging protocol and traffic characteristics, without relying on vendor-specific identifiers. In this work, we propose a lightweight and efficient framework for IoT device identification based on machine learning. Our model utilises a Random Forest classifier in conjunction with a data-driven feature selection strategy that emphasises low-overhead features derived from packet headers and traffic flow statistics. The proposed approach achieves high classification performance, attaining 97.32% accuracy in identifying general device categories and 94.39% accuracy for specific device types. It also demonstrates approximately a 40% improvement in computational efficiency compared to traditional classifiers, making it well-suited for deployment in resource-constrained edge environments. We evaluate the model under various real-world conditions, including spatiotemporal traffic variations, changes in operational modes, and different sampling intervals. Comparative experiments with established classifiers—such as J48, SMO, BayesNet, and Naive Bayes—are performed using standard metrics, including precision, recall, F1-score, and inference latency. Our approach strengthens network security by automatically identifying and classifying IoT devices in dynamic, heterogeneous environments. It is lightweight, scalable, and well-suited for deployment in resource-constrained IoT scenarios. • The research focuses on using device fingerprints and machine learning techniques to efficiently identify and classify IoT devices in heterogeneous networks. • The study employs Random Forest as the primary classification algorithm, achieving an accuracy of 97.32% for classifying IoT devices and 94.39% for identifying specific device types. • By leveraging optimized feature selection techniques, the study ensures high accuracy and reduces computational overhead. • The study highlights the importance of balancing accuracy and time efficiency in IoT device identification, offering a robust model for real-world applications.