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Network Traffic Anomaly Detection and Privacy Security Protection in Sensor Networks with 2D and 3D Image Fusion Using Machine Learning and Blockchain
Jing Liu, Yan Li and Hongling Yan

Abnormal network traffic events occur frequently, and the methods and means that lead to abnormal network traffic are constantly updated, making traditional digital identification and network attack protection more difficult. As network security faces numerous challenges, detecting abnormal traffic and protecting privacy are both difficult tasks, particularly in sensor networks. It is necessary to introduce new technologies, methods, and perspectives, such as machine learning and blockchain, to solve some problems. Therefore, this paper expands the research content of network abnormal traffic detection and privacy protection based on machine learning and blockchain technology, proposes a network abnormal traffic detection model and privacy protection algorithm for sensor networks, and illustrates the effectiveness and advantages of this method with experiments. The experimental results also demonstrate that this method has a significant impact on anomaly detection and privacy protection.

Keywords: network traffic, anomaly detection, privacy protection, sensor networks, machine learning (ML), blockchain, Artificial Intelligence; Enhanced 3D Modeling for Networks; 2D and 3D image fusion