IoT Devices Discovery and Identification Using Network Traffic Data

The Internet of Things (IoT) has been erupting world widely over the decade. However, the security and privacy leakage issues from IoT devices are surfaced to a major flaw for IoT device operators. An attacker may use network traffic data to identify IoT devices and launch attacks on their target devices. To explore the severity and extent of this privacy threat, we design a hybrid ML-based IoT device identification framework. Our key insight is that typically an IoT device has a unique traffic signature and it is already embedded in its network traffic. Unlike other existing work using complex modeling, we show that the majority of IoT devices can be easily identified using our empirical models, and the other devices can also be correctly classified using our ML-based models. We instrument a smart IoT experiment environment to verify and evaluate our approaches. Our framework paves the way for operators of smart homes to monitor the functionality, security and privacy threat without requiring any additional devices.