Unmasking IoT Devices: A Dynamic and Adaptive Classification Approach

The widespread adoption of IoT devices has transformed automation and connectivity across multiple sectors. These devices depend heavily on wireless communication, providing flexibility and enabling real-time data exchange. The unique traffic patterns they generate, shaped by protocol usage, communication frequency, and data exchange behaviors facilitate precise device identification. These patterns can be leveraged to classify devices based solely on their network activity. Current approaches to IoT device identification rely on single machine-learning classifiers trained on historical traffic data. However, these systems face critical limitations, including an inability to detect new devices, failure to adapt to shifting traffic patterns, and challenges with imbalanced data representation, thus requiring frequent re-training of the classifier. To address these challenges, we propose UMIoT, a dynamic and adaptive Multi-Classifier based framework for device identification. Unlike traditional approaches, UMIoT employs a unique classifier for each device, enhancing identification accuracy and adaptability. Our study evaluates UMIoT's performance and compares with existing device identification approaches using metrics such as accuracy, precision, recall, and F1-score. Our experiments demonstrate that UMIoT achieves 100% accuracy for most devices, with an overall average device identification accuracy of 95.58% across the network. Despite employing multiple classifiers to determine the origin of traffic, UMIoT maintains a low prediction time of 0.2 seconds for 1000 packets, comparable to state-of-theart single-classifier approaches. Additionally, with UMIoT's 60% classifier accuracy threshold, devices entering the network are accurately detected as new devices without misclassifying their traffic as originating from existing devices.