5G Internet of Things security gateway equipment based on electric power dedicated longitudinal dense chip

Abstract

The invention relates to a 5G Internet of Things security gateway device based on a power-dedicated longitudinal encryption chip, and the device comprises an embedded eSIM chip, a 5G communication module, a longitudinal encryption module with a built-in power miniature longitudinal encryption chip, a telecontrol function module, and an automatic power control (AGC) module. An entanglement perception encryption algorithm formed based on a heterogeneous graph neural network and a generative adversarial network is adopted between the longitudinal encryption module and the scheduling master station longitudinal encryption device, so that an encryption tunnel is established, identity bidirectional authentication, data encryption and access control functions are realized, and high security of data is realized.

Description

Technical Field

[0001] The present invention relates to a 5G Internet of Things security gateway device, and in particular to a 5G Internet of Things security gateway device based on a power-specific vertical density chip, and belongs to the field of 5G communication gateways. Background Art

[0002] The physical layer security technology (PLS) that has emerged in recent years uses the physical characteristics of wireless channels to solve communication security problems, bringing new ideas to ensure the security of heterogeneous networks. At present, the physical layer security research on heterogeneous networks can be roughly divided into the following two categories: (1) physical layer security modeling and performance analysis in heterogeneous networks; (2) physical layer security performance optimization in heterogeneous networks. Lee S et al. considered the issue of network security energy efficiency. In 2013, S Lee analyzed the impact of energy harvesting (EH) technology on the throughput of networks with different priority levels and the probability of user connection interruption. In 2016, Lv T et al. took the lead in studying the optimization of secure beamforming in heterogeneous networks.

[0003] In terms of lightweight encryption and authentication technology at the perception layer of the Internet of Things, classical cryptographic algorithms provide theoretical support and technical guidance for the design and security analysis of lightweight cryptographic algorithms; however, compared with traditional desktop computers and high-performance computers, the resource environment of these devices is usually limited, such as weak computing power, less storage available for computing, limited energy consumption, etc. Traditional cryptographic algorithms cannot be well adapted to such environments, which makes the research on cryptographic algorithms in restricted environments a hot issue that urgently needs to be solved. Some scholars have highly optimized existing standard block cipher algorithms such as AES and IDEA and tried to implement them concisely for hardware platforms, hoping to reduce the implementation resources to within the range allowed by RFID, but the effect is not ideal. In addition, there are also slight modifications to the classical algorithms to make them suitable for use in lightweight environments, such as improving the S box based on DES to produce the DESL algorithm. There are also block cipher algorithms designed specifically for low-resource environments, such as HIGHT, mCrypton, TEA, PRESENT, KATAN, KTANTAN and PRINT. The PRESENT block cipher was first published in CHES2007. LBlock was designed by Wu Wenling and Zhang Lei from my country and published in ACNS2011.

[0004] Many domestic scholars have also studied the physical layer security issues of heterogeneous networks. Accurately and comprehensively extracting security situation elements in the network is the basis of network security situation awareness research. However, since the network has developed into a large nonlinear complex system with strong flexibility, it is very difficult to extract network security situation elements. Therefore, how to solve the data security problem is a relatively complex issue. Summary of the invention

[0005] In view of the above problems of the prior art, we consider an asymmetric vertical encryption technology that imitates quantum entanglement for the 5G IoT security gateway of power equipment. This technology divides the key into two groups of keys that can continuously perceive each other's changes, namely public keys and private keys. To this end, we consider the following key technical elements: first, the data source of the key comes from the real-time gateway input data; second, according to the real-time input data, the public key is designed, and the corresponding private key is calculated according to a specific algorithm to form a perception mechanism of the private key to the public key, breaking away from the complexity of the network security situation awareness circle and changing to the perception inside the key; third, the timing of the perception change of the key. Therefore, we use the real-time data unknowability selected for the public key design, the method unknowability of the public key design, the algorithm unknowability of the private key calculation, and the unknowability of the timing of the perception change, such multiple unknowability to construct a vertical encryption mechanism.

[0006] Based on the above considerations, the present invention provides a 5G Internet of Things security gateway device based on a power-specific vertical density chip, including: Embedded eSIM chip: including an eSIM chip-based identity card embedded in a circuit board; 5G communication module: It has a built-in 5G communication module dedicated to power, sets the SA independent networking mode, connects to the 5G power slice private network through the eSIM chip, and then connects to the dispatching master station security access area to achieve communication with the dispatching master station private network; Vertical encryption module: Built-in power micro-vertical encryption chip, and the dispatching main station vertical encryption device use entanglement-aware encryption algorithm to establish an encrypted tunnel, realizing two-way identity authentication, data encryption and access control functions; Telecontrol function module: can collect analog quantity and status quantity of the operation status in the station, monitor and upload to the dispatching master station, and execute the control and adjustment commands sent by the dispatching master station to other substations; Automatic power control function (AGC) module: It can receive and automatically execute the power control signal sent remotely by the dispatching master station, control the power output of the station according to the optimal strategy, ensure that the operation of the station meets the requirements of the dispatching master station, and at the same time transmit the system operation adjustment status and parameter information to the dispatching master station. The entanglement-aware encryption algorithm comprises the following steps: S1 constructs the 5G power slice private network into a heterogeneous graph neural network, treating each layer of slices as a node in the heterogeneous graph neural network; S2 sets a temporary data storage unit in the node, collects historical data before the current moment as a reference point, analyzes three types of weight coefficients of neighbors, paths, and subgraphs around the preset node based on the historical data, and selects neighbors, paths, and subgraphs whose three types of weight coefficients are within a preset range; S3 retrieves historical data within a preset time period from at least one of the selected neighbors, paths, and subgraphs, and randomly selects different text, pictures, videos, and audio parts from the historical data according to different types, i.e., text, pictures, videos, and audios, and splices them, and then forms a public key together with the corresponding text, pictures, videos, and audios defined by the user; S4 uses a pre-trained generative adversarial network (GAN) to input the spliced ​​text, picture, video, and audio, output the corresponding first image, and then input the corresponding text, picture, video, and audio customized by the user, and output the second image. The first image and the second image are used as private keys.

[0007] Therefore, historical data is selected for nodes, paths, and subgraphs of specific heterogeneous networks within a specific period of time, and random splicing is completed, and finally the corresponding private key of perception is formed according to the GAN mechanism.

[0008] The expressions of the three types of weight coefficients for neighbors, paths, and subgraphs are: ,in is a deep neural network with node attention, are the vector representations of the preset node and its neighbors, , is the weight of the meta-path set , is a node set, are attention vector, weight, and bias representation respectively. Concatenate feature representations for meta-paths repeated multiple times for the attention mechanism , is the subgraph weight, is a node set, are the corresponding attention vector, weight, and bias representations respectively. Beneficial Effects

[0009] Through the nodeization of 5G slices, a heterogeneous graph neural network is constructed. The weights of the preset nodes at the three levels of nodes, paths, and subgraphs are calculated through the attention mechanism, and specific neighbors, paths, and subgraphs are selected. The historical data of the specified viewpoints stored therein are randomly spliced ​​to obtain the public key, and the public key is substituted into the GAN mechanism to form a private key. In this way, the multivariate unknowable technology is used to form a convenient encryption algorithm that is difficult to obtain but difficult to crack. At the same time, it is changed regularly. The public key simulates the change of the quantum entangled state with the private key to ensure the high security of the data. There is no need to consider the data security situation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The present invention provides a 5G Internet of Things security gateway device configuration diagram based on a power-specific vertical density chip; Figure 2 Flowchart of the entanglement-aware encryption algorithm. DETAILED DESCRIPTION

[0011] Figure 1 The present invention provides a 5G Internet of Things security gateway device configuration diagram based on a power-specific vertical density chip, including: Embedded eSIM chip: including an eSIM chip-based identity card embedded in a circuit board; 5G communication module: It has a built-in power-specific 5G communication module, sets the SA independent networking mode, connects to the 5G power slice private network through the eSIM chip, and then connects to the dispatching master station security access area through the vertical encryption module to achieve communication with the dispatching master station private network; Longitudinal encryption module: It has a built-in electric micro-longitudinal encryption chip, and uses the entanglement-aware encryption algorithm to establish an encrypted tunnel with the longitudinal encryption device of the dispatching main station, realizing two-way identity authentication, data encryption and access control functions; Telecontrol function module: It can collect analog quantities and status quantities of the operating status of power station equipment in the power station, monitor and upload them to the dispatching master station, and execute the control and adjustment commands sent by the dispatching master station to other substations, and finally realize the functions of "telecommunication, telemetry, remote control, and remote adjustment"; Automatic power control function (AGC) module: It can accept and automatically execute the active and reactive power control signals sent remotely by the dispatching master station to control the power output of this station according to the optimal strategy, ensure that the operation of this station meets the requirements of the dispatching master station, and at the same time transmit the system operation adjustment status and parameter information to the dispatching master station.

[0012] Figure 2 is a flow chart of the entanglement-aware encryption algorithm, comprising the following steps: S1 constructs the 5G power slice private network into a heterogeneous graph neural network, treating each layer of slices as a node in the heterogeneous graph neural network; S2 sets a temporary data storage unit in the node, collects historical data before the current moment as a reference point, analyzes three types of weight coefficients of neighbors, paths, and subgraphs around the preset node based on the historical data, and selects neighbors, paths, and subgraphs whose three types of weight coefficients are within a preset range; S3 calls out historical data within 1 to 10 days from the selected neighbors, paths, and subgraphs, and randomly selects different text, pictures, videos, and audio parts from the historical data according to the type of the historical data, i.e., text, pictures, videos, and audios, and then forms a public key together with the corresponding text, pictures, videos, and audios customized by the user; Figure 2 The example of splicing image and audio data is given in which a plurality of sub-blocks are divided in the image, and then randomly selected and spliced ​​in the order of random selection, with a user-defined image (which can be a user's face) as the public key.

[0013] S4 uses a pre-trained generative adversarial network (GAN) to take the input spliced ​​pictures and audios as examples, and output the corresponding first images respectively, and then inputs the corresponding pictures and audios customized by the user as examples, and outputs the second image. The first image and the second image are used as private keys.

[0014] Among them, the expressions of the three types of weight coefficients of neighbors, paths, and subgraphs in step S2 are: is a deep neural network with node attention, are the vector representations of the preset node and its neighbors, , is the weight of the meta-path set , is a node set, are attention vector, weight, and bias representation respectively. Concatenate feature representations for meta-paths repeated multiple times for the attention mechanism , is the subgraph weight, is a node set, are the corresponding attention vector, weight, and bias representations respectively.

Claims

1. 5G IoT security gateway device based on power-specific vertical density chip, Features ,include: Embedded eSIM chip: including an eSIM chip-based identity card embedded in a circuit board; 5G communication module: It has a built-in 5G communication module dedicated to power, sets the SA independent networking mode, connects to the 5G power slice private network through the eSIM chip, and then connects to the dispatching master station security access area to achieve communication with the dispatching master station private network; Vertical encryption module: Built-in power micro-vertical encryption chip, and the dispatching main station vertical encryption device use entanglement-aware encryption algorithm to establish an encrypted tunnel, realizing two-way identity authentication, data encryption and access control functions; Telecontrol function module: can collect analog quantity and status quantity of the operation status in the station, monitor and upload to the dispatching master station, and execute the control and adjustment commands sent by the dispatching master station to other substations; Automatic power control function (AGC) module: It can accept and automatically execute the active and reactive power control signals sent remotely by the dispatching master station, control the power output of the station according to the optimal strategy, ensure that the operation of the station meets the requirements of the dispatching master station, and transmit the system operation adjustment status and parameter information to the dispatching master station. The entanglement-aware encryption algorithm comprises the following steps: S1 constructs the 5G power slice private network into a heterogeneous graph neural network, treating each layer of slices as a node in the heterogeneous graph neural network; S2 sets a temporary data storage unit in the node, collects historical data before the current moment as a reference point, analyzes three types of weight coefficients of neighbors, paths, and subgraphs around the preset node based on the historical data, and selects neighbors, paths, and subgraphs whose three types of weight coefficients are within a preset range; S3 retrieves historical data within a preset period of 1-10 days from at least one of the selected neighbors, paths, and subgraphs, and randomly selects different text, pictures, videos, and audio parts from the historical data according to different types, i.e., text, pictures, videos, and audios, and splices them, and then forms a public key together with the corresponding text, pictures, videos, and audios defined by the user; S4 uses a pre-trained generative adversarial network (GAN) to input the spliced ​​text, picture, video, and audio, output the corresponding first image, and then input the corresponding text, picture, video, and audio customized by the user, and output the second image. The first image and the second image are used as private keys, where: The expressions of the three types of weight coefficients for neighbors, paths, and subgraphs are: ,in is a deep neural network with node attention, are the vector representations of the preset node and its neighbors, , is the weight of the meta-path set , is a node set, are attention vector, weight, and bias representation respectively. Concatenate feature representations for meta-paths repeated multiple times for the attention mechanism , is the subgraph weight, is a node set, are the corresponding attention vector, weight, and bias representations respectively.

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