Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of Things

Abstract

With the development of the Energy Internet of Things (EIoT), it is of great practical significance to study the security strategy and intelligent control system for solar thermal utilization system to optimize the operation efficiency and carry out intelligent dynamic adjustment. For buildings integrated with solar water heating systems, computational fluid dynamics simulation was used in analyzing the process of solar energy output. A method based on machine learning is proposed to predict energy conversion. Besides, the simulation and analysis are carried out in combination with the possible safety problems such as the vibration of the control system. This paper proposed a novel platform of EIoT for machine learning-based cybersecurity study and implemented the platform for the temperature monitoring system. After the evaluation of the machine learning-based cybersecurity study, the EIoT system demonstrated a high performance with the Extreme Gradient Boosting (XGBoost) training algorithm.

Research on Machine Learning Algorithm for Internet of Things Information Security Management System Research and Implementation

“…As an important information technology, the Internet of Things has been widely used in all walks of life, such as the power system, water supply enterprises, transportation departments, and intelligent life, all of which exist in the application of the Internet of Things technology [1]. At present, the Business of the Internet of Things develops very rapidly in China, and the scale of the card opening of the Internet of Things is also increasing day by day.…”

Security, Trust, and Privacy in Machine Learning-Based Internet of Things

“…In the next contribution titled "Machine Learning-Based Stealing Attack of the Temperature Monitoring System for the Energy Internet of ings", Li et al [24] designed a platform of Energy Internet of ings (EIoT) for the temperature monitoring system. ey then introduced a twostep model stealing attack that can use the stolen data to set a copycat network, which could leak the artificial intelligence models.…”