Research On Application Of Artificial Intelligence In Grid Embedded Terminal Security Detection

With the continuous development of embedded technology,embedded devices have been widely used in different fields such as industrial control,traffic management,and consumer electronics.In power industry,the popularity of embedded terminals has made the smart grid more automated and intelligent.Not only has it improved the energy efficiency,but also brought better consumer experience to power consumers.However,while achieving efficient power monitoring,regulation,and control,embedded terminals brought new security challenges to the power grid due to the continuous escalation of cyber-attacks.Once terminals are invaded,it will cause irreparable losses to the power production.Traditional security detection methods for the embedded terminals require preset rules by the experts,which rely heavily on the experts' knowledge,and blind spots occurs frequently.At the same time,these rule-based detection methods need to be updated over time,which costs a lot of human resources.On the other side,artificial intelligence technology has been introduced into many security detection scenarios and achieved satisfactory results with its excellent feature mining capabilities and autonomous learning capabilities.Therefore,it is of great significance to study the application of artificial intelligence technology in security detection of embedded terminals in power grids.This paper proposes a machine learning based security detection technology to achieve comprehensive security detection of power grid embedded terminals.For the smart meters frequently used in detecting power consumption,this paper proposes a security detection method in RPL protocol to protect terminals from black hole attacks and ensure the reliability of terminal communication.For the traditional grid embedded terminals,this paper proposes a side-channel based security detection method.This method uses the LSTM neural network with the attention mechanism to learn the model of the long normal sequence.We can perform security detection based on the difference between the actual collected data and the model prediction.For the new generation of embedded terminals in the smart grid,this paper proposes an equipment operating status based security detection scheme,using the wide & deep model to analyze the running status of the equipment to achieve fine-grained security detection.This paper verifies the feasibility of the proposed method with real world devices and attack samples.The experimental results show that the artificial intelligence models proposed in this paper can effectively detect the security status of embedded terminals in the power grid.