Under the current background, it is very important to study the key technologies of new power system edge-to-side security protection for massive heterogeneous power IoT terminals and edge IoT agents, including defense technologies at the levels of device ontology security, communication interaction security, and secure access. Meaning. The new power system edge-to-side security protection technology has a summary impact on the privacy protection of indoor positioning. This paper proposes an indoor positioning privacy protection method based on federated learning in Mobile Edge. Computing (MEC) environment. Firstly, we analyze the learning mechanisms of horizontal, vertical, and transfer-federated learning, respectively, and mathematically describe it based on the applicability of horizontal and vertical-federated learning under different sample data characteristics. Then, the risk of data leakage when data are used for research or analysis is greatly reduced by introducing differential privacy. In addition, considering the positioning performance, privacy protection, and resource overhead, we further propose an indoor positioning privacy protection model based on federated learning and corresponding algorithms in MEC environment. Finally, through simulation experiments, the proposed algorithm and other three algorithms are, respectively, compared and analyzed in the case of two identical datasets. The experimental results show that the convergence speed, localization time consumption, and localization accuracy of the proposed algorithm are all optimal. Moreover, its final positioning accuracy is about 94%, the average positioning time is 250 ms, and the performance is better than the other three comparison algorithms.
Recently, IoT devices have become the targets of large-scale cyberattacks, and their security issues have been increasingly serious. However, due to the limited memory and battery power of IoT devices, it is hardly possible to install traditional security software, such as antivirus software for security defense. Meanwhile, network-based traffic detection is difficult to obtain the internal behavior states and conduct in-depth security analysis because more and more IoT devices use encrypted traffic. Therefore, how to obtain complex security behaviors and states inside IoT devices and perform security detection and defense is an issue that needs to be solved urgently. Aiming at this issue, we propose IoT-DeepSense, a behavioral security detection system of IoT devices based on firmware virtualization and deep learning. IoT-DeepSense constructs the real operating environment of the IoT device system to capture the fine-grained system behaviors and then leverages an LSTM-based IoT system behavior abnormality detection approach to effectively extract the hidden features of the system’s behavior sequence and enforce the security detection of the abnormal behavior of the IoT devices. The design and implementation of IoT-DeepSense are carried out on an independent Internet of things behavior detection server, without modifying the limited resources of IoT devices, and have strong scalability. The evaluation results show that IoT-DeepSense achieves a high behavioral detection rate of 92%, with negligible impact on the performance of IoT devices.
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