Secure and Trustworthy Data Management Mechanism for Dance-Consumer Electronics in AIoT

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) marks a considerable advancement in data collection and utilization across various sectors. In the realm of complex scenarios, dance-consumer electronics (DCE) employ this technology to provide detailed and trustworthy documentation and three-dimensional digital capture of dancers’ movements. However, the handling of sensitive physiological data poses significant security and trustworthiness risks in such consumer electronics, including potential breaches that could be exploited for location tracking and behavior prediction. To address the challenges of encrypted traffic detection and the need for trustworthiness in IoT, this paper introduces the AI-Enhanced Anonymous Traffic Filtering Framework, which features terminal anonymization. This framework employs the Inception-SelfAttBiGRU model to filter malicious traffic and includes a credibility-based filtering mechanism prior to this module to reduce network load and enhance system efficiency for those consumer electronics. The evaluation concentrated on two main areas: assessing the access credibility-based filtering mechanism’s ability to identify and block malicious DCE endpoints and evaluating the effectiveness of the encrypted traffic classification technique. Using simulated environments and public datasets, this comprehensive assessment demonstrated that the dual-layer protection mechanism effectively detects and blocks malicious traffic, thereby enhancing system security and maintaining network integrity.