Wireless Spy Camera Spotter System With Real-Time Traffic Similarity Analysis and WiFi Signal Tracing

This paper introduces a novel method to detect WiFi-enabled spy cameras potentially invading user privacy via wireless streaming within a user's vicinity. So far, there have been social initiatives to conduct spy camera inspections to address this issue, and academic societies have been working on various detection methods. However, such studies typically rely on additional specific efforts, such as using specialized equipment to gather channel state information (CSI) or collecting extensive long-term data per location for machine learning approaches. In addition, some ideas could not recognize features of spy camera data due to data encryption. Hence, this paper comes up with a novel scheme for detecting wireless spy cameras based only on off-the-shelf devices. To achieve these functionalities, the proposed scheme consists of three phases: 1) classification, 2) detection, and 3) localization. First, in the classification phase, the collected packet headers are analyzed to identify and classify the upstream streaming protocols. In addition, the proposed scheme fulfills the detection phase that exploits the Nilsimsa algorithm to analyze packet similarity, identifying potential spy cameras by detecting consistent high similarity patterns in the classified data traffic. Finally, the proposed approach provides the localization phase that captures and analyzes Received Signal Strength Indication (RSSI) values at the target location and spots the camera position. Unlike existing methods, the proposed scheme does not rely on extensive data collection per location and complex mechanisms to interpret encrypted data. Moreover, compared to machine learning (ML)-based positioning means, which require significant data sets and long-term computation time, the scheme achieves real-time detection with minimal computational demands.