5G Internet of Things Security Gateway Device Based on Power-Specific Vertical Encryption Chip

Abstract

The present invention relates to a 5G Internet of Things security gateway device based on a dedicated power longitudinal encryption chip, which includes an embedded eSIM chip, a 5G communication module, a longitudinal encryption module with a built-in power micro longitudinal encryption chip, a telecontrol function module, and an automatic generation control (AGC) module. An entanglement-aware encryption algorithm formed based on a heterogeneous graph neural network and a generative adversarial network is adopted between the longitudinal encryption module and the longitudinal encryption device of the dispatching master station to establish an encrypted tunnel, realizing functions of two-way identity authentication, data encryption, and access control, and achieving highly secure data.

Description

Technical Field

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

[0002] In recent years, the emerging physical layer security technology (Physical Layer Security, PLS) uses the physical characteristics of wireless channels to solve communication security problems, bringing new ideas for ensuring the security of heterogeneous networks. Currently, the physical layer security research for 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 problem of secure energy efficiency of the network. In 2013, S Lee analyzed the impact of energy harvesting (EH) technology on the throughput of different priority-level networks and the connection interruption probability of users. In 2016, Lv T et al. pioneered the research on secure beamforming optimization in heterogeneous networks.

[0003] In terms of lightweight encryption and authentication technology in the Internet of Things sensing layer, classical cryptographic algorithms provide theoretical support and technical guidance for the design and security analysis of lightweight cryptographic algorithms; however, compared with traditional desktops and high-performance computers, the resource environments of these devices are usually limited. For example, they have weak computing capabilities, less storage available for computing, limited energy consumption, and so on. Traditional cryptographic algorithms cannot be well applied to such an environment, which makes the research on cryptographic algorithms in restricted environments an urgent hot issue to be solved. Some scholars highly optimized existing standard block cipher algorithms such as AES and IDEA and tried to achieve a concise implementation for hardware platforms, expecting to reduce the implementation resources to within the range allowed by RFID, but the effect was not ideal. In addition, there were also some slight modifications to classical algorithms to make them suitable for lightweight environments, such as improving the S-box on the basis of DES to generate the DESL algorithm. There were 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 in China 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:

[0007] Embedded eSIM chip: including an eSIM chip-based identity card embedded in a circuit board;

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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.

[0012] The entanglement-aware encryption algorithm includes the following steps:

[0013] S1 Construct a heterogeneous graph neural network for the 5G power slicing private network, and regard each layer of slice as a node in the heterogeneous graph neural network;

[0014] S2 Set a temporary data storage unit in the nodes, collect historical data before the current moment as the reference point, analyze three types of weight coefficients of the preset neighbors, paths, and subgraphs around the nodes based on the historical data, and select neighbors, paths, and subgraphs with the three types of weight coefficients within the preset range;

[0015] S3 Retrieve historical data within a preset time period from at least one of the selected neighbors, paths, and subgraphs. According to the different types of historical data, namely text, pictures, videos, and audio, randomly select different text, picture, video, and audio parts for splicing, and then form a public key together with the corresponding text, pictures, videos, and audio defined by the user;

[0016] S4 Use a pre-trained generative adversarial network (GAN) to input the spliced text, pictures, videos, and audio, output the corresponding first image, and then input the corresponding text, pictures, videos, and audio defined by the user to output a second image. Then, the first image and the second image are used as the private key.

[0017] Therefore, within a specific time period, historical data is selected for the nodes, paths, and subgraphs of a specific heterogeneous network, and random splicing is completed. Finally, the corresponding private key for perception is formed according to the GAN mechanism.

[0018] The expressions for the three types of weight coefficients of neighbors, paths, and subgraphs are respectively: , where is a deep neural network for node attention, are respectively the vector representations of the preset node and its neighbor, , is the weight of the meta-path set , is the node set, are respectively the attention vector, weight, and bias representations, is the meta-path splicing feature representation obtained by repeating the attention mechanism multiple times , is the subgraph weight, is the node set, are respectively the corresponding attention vector, weight, and bias representations. Beneficial effects

[0019] Through the node-based 5G slicing, a heterogeneous graph neural network is constructed. The weights of the preset nodes at the node, path, and subgraph levels are calculated through the attention mechanism, specific neighbors, paths, and subgraphs are selected, and the specified viewpoint historical data stored therein is randomly spliced to obtain a public key, and the public key is substituted into the GAN mechanism to form a private key. Thus, a multi-faceted unknowable technology is used to form an encryption algorithm that is difficult to obtain data elements but difficult to crack, and is also changed regularly. The public key changes with the private key to simulate the quantum entanglement state, ensuring the high security of the data. There is no need to consider the data security situation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention provides a configuration diagram of a 5G Internet of Things security gateway device based on a power-specific longitudinal encryption chip;

[0021] Figure 2 Flowchart of the entanglement-aware encryption algorithm. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Figure 1 The present invention provides a configuration diagram of a 5G Internet of Things security gateway device based on a power-specific longitudinal encryption chip, including:

[0023] Embedded eSIM chip: including an identity card based on the eSIM chip embedded in the circuit board;

[0024] 5G communication module: It is built with a power-specific 5G communication module, set in the SA independent networking mode, connected to the 5G power slice private network through the eSIM chip, and then connected to the dispatching master station security access area through the longitudinal encryption module to realize communication with the dispatching master station private network;

[0025] Longitudinal encryption module: Built with a power micro longitudinal encryption chip, an encryption tunnel is established with the longitudinal encryption device of the dispatching master station using the entanglement-aware encryption algorithm to realize functions of two-way identity authentication, data encryption, and access control;

[0026] Telecontrol function module: It can collect analog and status quantities of the operation status of power station equipment in the power station, monitor and upload these to the dispatching master station, and execute control and adjustment commands sent by the dispatching master station to other sub-stations, ultimately realizing the functions of "remote signaling, remote measurement, remote control, and remote adjustment";

[0027] Automatic generation control (AGC) function module: It can accept and automatically execute the active and reactive power control signals remotely sent 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 back to the dispatching master station.

[0028] Figure 2 The flowchart of the entanglement-aware encryption algorithm is as follows:

[0029] S1 constructs the 5G power slicing private network into a heterogeneous graph neural network, regarding each layer of slices as nodes in the heterogeneous graph neural network;

[0030] S2 sets up a temporary data storage unit in the nodes, collects historical data before the current moment as the reference point, analyzes three types of weight coefficients of the preset neighbors, paths, and subgraphs around the node based on the historical data, and selects neighbors, paths, and subgraphs with the three types of weight coefficients within the preset range;

[0031] S3 retrieves historical data within 1 to 10 days from the selected neighbors, paths, and subgraphs. According to the different types of historical data, namely text, pictures, videos, and audio, randomly select different parts of text, pictures, videos, and audio for splicing, and then form a public key together with the corresponding text, pictures, videos, and audio defined by the user;

[0032] Figure 2 The splicing method for picture and audio data is exemplified. Divide multiple sub - picture blocks in the picture, then randomly select them and splice them in the order of random selection, and use the user - defined picture (which can be the user's face) as the public key.

[0033] S4 takes the spliced pictures and audio input by using a pre - trained generative adversarial network (GAN) as examples, respectively outputs the corresponding first images, and then takes the corresponding pictures and audio defined by the user as examples and outputs the second images. Then the first images and the second images are used as private keys.

[0034] Among them, the expressions of the three types of weight coefficients of neighbors, paths, and subgraphs in step S2 are respectively: is the deep neural network of node attention, are respectively the vector representations of the preset node and its neighbors, , is the weight of the meta - path set , is the node set, are respectively the attention vector, weight, and bias representations, is the meta - path splicing feature representation of the attention mechanism repeated multiple times , is the sub - graph weight, is the node set, are respectively the corresponding attention vector, weight, and bias representations.

Claims

1. A 5G Internet of Things security gateway device based on a dedicated power vertical encryption chip, characterized in that , including: Embedded eSIM chip: including an identification card based on the embedded eSIM chip embedded in a circuit board; 5G communication module: its built-in power dedicated 5G communication module, set in the SA independent networking mode, connected to the 5G power slice private network through the embedded eSIM chip, and then connected to the secure access area of the dispatching master station to achieve communication with the dispatching master station private network; Vertical encryption module: built-in power micro vertical encryption chip, and the vertical encryption device of the dispatching master station uses the entanglement-aware encryption algorithm to establish an encrypted tunnel to achieve functions of two-way identity authentication, data encryption and access control; Telecontrol function module: can collect analog and status quantities of the operation status within the station, monitor and upload to the dispatching master station, and execute control and adjustment commands sent by the dispatching master station to other substations; Automatic power control function (AGC) module: can receive and automatically execute active and reactive power control signals remotely sent by the dispatching master station, control the power output of the station according to the optimal strategy to ensure that the operation of the station meets the requirements of the dispatching master station, and at the same time send back the operation adjustment status and parameter information of the system to the dispatching master station, where The entanglement-aware encryption algorithm includes the following steps: S1 Construct the 5G power slice private network into a heterogeneous graph neural network, and regard each layer of slice as a node in the heterogeneous graph neural network; S2 Set a temporary data storage unit in the node, collect historical data before the current moment as the reference point, analyze three types of weight coefficients of the preset neighbors, paths, and subgraphs around the preset node based on the historical data, and select neighbors, paths, and subgraphs with the three types of weight coefficients within the preset range; S3 Retrieve historical data within a preset 1-10 days from at least one of the selected neighbors, paths, and subgraphs. According to the different types of historical data, namely text, pictures, videos, and audio, randomly select different parts of text, pictures, videos, and audio for splicing, and then form a public key together with the corresponding text, pictures, videos, and audio defined by the user; S4 Use the pre-trained generative adversarial network (GAN) to input the spliced text, pictures, videos, and audio, output the corresponding first image, and then input the corresponding text, pictures, videos, and audio defined by the user to output the second image. Then the first image and the second image are used as private keys, where The expressions for the three types of weight coefficients of neighbors, paths, and subgraphs are as follows: , where is a deep neural network for node attention, are the vector representations of the preset node and its neighbors, respectively; , is the weight of the meta-path set, , is the node set for calculating the meta-path set, are the attention vector, weight, and bias representations, respectively, is the meta-path concatenation feature representation with the attention mechanism repeated multiple times; , is the subgraph weight, , is the node set for calculating the subgraph weight, are the corresponding attention vector, weight, and bias representations, respectively.

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