Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contexts
This work addresses network monitoring and security for IoT systems by improving identification accuracy in real-world scenarios, though it is incremental as it builds on existing machine learning methods with a new feature.
The paper tackled the problem of IoT device identification in dynamic wireless environments by using device latency and a novel accumulation score to capture channel dynamics, achieving an F1 score of over 97%, a significant improvement from 75% without considering dynamics.
Identifying IoT devices is crucial for network monitoring, security enforcement, and inventory tracking. However, most existing identification methods rely on deep packet inspection, which raises privacy concerns and adds computational complexity. More importantly, existing works overlook the impact of wireless channel dynamics on the accuracy of layer-2 features, thereby limiting their effectiveness in real-world scenarios. In this work, we define and use the latency of specific probe-response packet exchanges, referred to as "device latency," as the main feature for device identification. Additionally, we reveal the critical impact of wireless channel dynamics on the accuracy of device identification based on device latency. Specifically, this work introduces "accumulation score" as a novel approach to capturing fine-grained channel dynamics and their impact on device latency when training machine learning models. We implement the proposed methods and measure the accuracy and overhead of device identification in real-world scenarios. The results confirm that by incorporating the accumulation score for balanced data collection and training machine learning algorithms, we achieve an F1 score of over 97% for device identification, even amidst wireless channel dynamics, a significant improvement over the 75% F1 score achieved by disregarding the impact of channel dynamics on data collection and device latency.