An IoT Device Identification Method Using Extracted Fingerprint From Sequence of Traffic Grayscale Images

With the widespread deployment and application of various types of IoT devices, preventing illegal intrusion and impersonation attacks of IoT devices has become an important security challenge. Device identification helps to limit the behavior of suspicious devices and enhances the security of the device access process. In this paper, we propose a novel deep learning-based automatic fingerprint extraction model that addresses low efficiency and complexity of traditional feature engineering process, which are often rely on expert experience. The proposed model integrates advanced modules such as Depthwise Separable Convolution (DSC) and Gated Recurrent Unit (GRU), as well as architectures of inverted residuals and linear bottlenecks to enhance the performance of fingerprint extraction. After converting the raw device traffic into the sequence of traffic grayscale images, the model can analyze spatial and temporal features from them to generate highly distinguishable device fingerprints automatically. Additionally, we also achieve fast fingerprint search based on Hierarchical Navigable Small World (HNSW) to support device identification. Our proposed method can not only indicate deviations in device behavior from expected specifications, but also identify unknown and unreliable IoT devices. The experimental results show that our method has excellent performance and more comprehensive identification capabilities in multiple dimensions.