A detection method and system for a new power system LoRa wireless communication plaintext transmission vulnerability

Abstract

The present application belongs to the technical field of wireless communication security detection, and particularly relates to a detection method and system for LoRa wireless communication plaintext transmission vulnerability of a new power system, wherein a software-defined radio device is combined with GNURadio to collect LoRa signals conforming to the CN470 standard in the new power system, extract the center frequency, bandwidth parameters, and brute-force crack the spread spectrum factor; a custom-developed LoRa signal demodulation and decoding module is used to realize efficient demodulation and decoding of the LoRa PHY signal, and forward the UDP data packet to the local 58868 port; the Wireshark packet capture is combined with the Python script to analyze the mixed power system control / monitoring message in the message, verify whether the data transmission is in plaintext form, and determine whether the plaintext transmission vulnerability exists. The present application can solve the security technical problem of power monitoring and control data leakage and tampering caused by plaintext transmission of LoRa wireless communication in the new power system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication security detection technology, specifically relating to a method and system for detecting plaintext transmission vulnerabilities in LoRa wireless communication in new power systems. It is particularly applicable to the detection of plaintext transmission vulnerabilities in LoRa wireless communication systems conforming to the CN470 standard in scenarios such as the distribution side, consumption side, and new energy grid connection side of new power systems. Background Technology

[0002] The new power system is based on new energy sources and integrates multiple links such as power distribution, power consumption, new energy grid connection, and energy storage. As a low-power wide-area wireless communication technology, LoRa is widely used in scenarios such as power data monitoring, remote equipment control, and new energy grid connection status acquisition in the new power system due to its advantages of long transmission distance, low power consumption, and strong anti-interference ability. The CN470 standard is the mainstream frequency band standard for LoRa application in the domestic power system.

[0003] LoRa wireless communication employs proprietary spread spectrum methods derived from CSS linear frequency modulation spread spectrum and FEC forward error correction coding to enhance anti-interference capabilities. However, some LoRa communication links in new power systems do not encrypt critical data such as power operation data and equipment control commands, resulting in security vulnerabilities due to plaintext transmission. These critical data directly impact the stable operation of the new power system. Plaintext LoRa signals are easily intercepted and parsed by external devices, potentially leading to the leakage of critical data such as power monitoring data, renewable energy grid connection parameters, and distribution control commands. Furthermore, malicious attackers may tamper with the data and forge control commands, causing distribution faults, abnormal renewable energy grid connection, and malfunctions of consumer-side equipment, seriously threatening the safe and stable operation of the new power system.

[0004] Currently, LoRa wireless communication security detection technologies are mostly concentrated in general IoT scenarios, lacking customized detection methods for new power systems. Existing general detection technologies cannot adapt to the parsing requirements of various scenarios and types of power messages in new power systems. Meanwhile, while software-defined radio devices combined with GNU Radio can achieve wireless signal acquisition and analysis, existing general LoRa demodulation and decoding modules are difficult to adapt to the transmission characteristics of LoRa signals in new power systems. Therefore, developing a method and system for detecting LoRa wireless communication plaintext transmission vulnerabilities in new power systems is crucial for ensuring the security of wireless communication in these systems. Summary of the Invention

[0005] The purpose of this invention is to provide a detection method and system for plaintext transmission vulnerabilities in LoRa wireless communication in new power systems. This method and system can solve the technical problems of existing technologies being unable to adapt to the multi-scenario LoRa communication detection needs of new power systems, having difficulty in accurately parsing power messages, and being unable to effectively identify plaintext transmission vulnerabilities. This invention provides a customized and accurate detection solution for the security protection of LoRa wireless communication in new power systems.

[0006] The specific technical solution adopted by this invention is as follows: A method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for novel power systems, comprising the following steps: Multi-scenario signal acquisition stage: A signal acquisition architecture is built using software-defined wireless devices combined with GNURadio. The target frequency band of the software-defined wireless devices is set to the 470MHz range to adapt to LoRa communication scenarios in the power distribution, power consumption, and new energy grid connection of new power systems. The signal components within the frequency range are analyzed by FFT fast Fourier transform blocks, and the maximum holding frame of the FFT display is enabled to track the frequency components and capture the target LoRa signal. Signal parameter extraction and brute-force cracking stage: Extract the center frequency and bandwidth parameters from the captured target LoRa signal of the new power system. For the LoRa spreading factor SF values ​​of 6, 7, 8, 9, 10, 11, and 12, use a brute-force cracking method to determine the spreading factor of the target LoRa signal. Customized demodulation and decoding and data packet forwarding stage: Based on the custom-developed LoRa signal demodulation and decoding module, a LoRa signal demodulation and decoding architecture is built. The LoRaPHY signal collected in the multi-scenario signal acquisition stage is input to the LoRa signal demodulation and decoding architecture to complete signal demodulation and decoding. The decoded data packet is forwarded to port 58868 of the local host in UDP form. Power Message Parsing and Vulnerability Assessment Phase: UDP packets on port 58868 are monitored using Wireshark network packet capture tool. The message content in the packets is parsed to detect whether the packet payload segment contains new power system control / monitoring messages. If so, read / write commands are sent to the corresponding register of the Modbus slave address contained therein. The power operation data replied by the register is obtained and parsed to verify whether the data is transmitted in plaintext. If it is transmitted in plaintext, it is determined that the LoRa wireless communication of the new power system has a plaintext transmission vulnerability.

[0007] Furthermore, in the multi-scenario signal acquisition stage, the frequency coverage is expanded by adjusting the sampling rate (samp_rate), and the center frequency is dynamically changed by the slider of GNURadio, so as to achieve full coverage acquisition of LoRa signals in different scenarios and frequency bands of the new power system.

[0008] Furthermore, the target LoRa signal extracted during the signal parameter extraction and brute-force attack stage has a center frequency of 471.5MHz, a bandwidth of 250kHz, and a spreading factor of 10 determined after the brute-force attack.

[0009] Furthermore, the LoRa signal demodulation and decoding module in the customized demodulation and decoding and data packet forwarding stage is based on the characteristics of CSS linear frequency modulation spread spectrum and FEC forward error correction coding. It optimizes the demodulation algorithm for the LoRa signal transmission characteristics of CN470 standard and new power system, improves the accuracy of power message parsing, and realizes efficient demodulation and decoding of LoRaPHY signal.

[0010] Furthermore, in the power message parsing and vulnerability assessment stage, the Modbus slave address is address 01. A dedicated parsing tool is written to parse the new power system control / monitoring messages mixed in the message in real time, and to perform the operations of sending power system register instructions and parsing power operation reply data.

[0011] A detection system for LoRa wireless communication plaintext transmission vulnerabilities in new power systems is provided. The system is used to implement a method for detecting LoRa wireless communication plaintext transmission vulnerabilities. It includes a signal acquisition module, a parameter analysis module, a demodulation and decoding module, and a message parsing module. The modules are interconnected and adapted to LoRa communication vulnerability detection in multiple scenarios of new power systems. The signal acquisition module consists of a software-defined wireless device and an acquisition architecture built with GNURadio. It is used to acquire LoRa signals in the 470MHz range of the new power system, adapting to acquisition scenarios on the power distribution, power consumption, and new energy grid connection sides. It analyzes frequency components through FFT blocks and enables maximum hold frame tracking of signal characteristics. The parameter analysis module is communicatively connected to the signal acquisition module and is used to extract the center frequency and bandwidth parameters from the target LoRa signal of the new power system and determine the spreading factor by brute-force cracking. The demodulation and decoding module is built on a custom-developed LoRa signal demodulation and decoding module and is connected to the parameter analysis module. It is used to demodulate and decode the LoRaPHY signal according to the determined signal parameters and forward the decoded UDP data packets to port 58868 of the local host. The message parsing module is communicatively connected to the demodulation and decoding module. It is used to monitor UDP data packets on port 58868, parse the relevant message content of the power system, verify whether the data is transmitted in plaintext, and determine whether there is a plaintext transmission vulnerability in the new power system LoRa wireless communication.

[0012] Furthermore, the signal acquisition module is configured with a sampling rate adjustment unit and a center frequency adjustment unit; the sampling rate adjustment unit is used to adjust the sample rate to expand the frequency coverage range, and the center frequency adjustment unit realizes dynamic adjustment of the center frequency through the GNURadio slider to adapt to the LoRa signal acquisition needs of different scenarios in the new power system.

[0013] Furthermore, the demodulation and decoding module supports LoRa proprietary spread spectrum methods derived from CSS linear frequency modulation spread spectrum and FEC forward error correction coding, and is compatible with 6-12 spread spectrum factor demodulation and decoding. The LoRa signal demodulation and decoding module optimizes demodulation accuracy and decoding efficiency for the CN470 standard and the transmission characteristics of LoRa signals in new power systems, and is compatible with power system message parsing.

[0014] Furthermore, the message parsing module includes a Wireshark packet capture module and a power system-specific Python data parsing submodule; the Wireshark packet capture module is used to monitor UDP packets on port 58868. The dedicated Python data parsing submodule for power systems has a built-in power message parsing script, which is used to parse control / monitoring messages in the message payload segment in real time, send instructions to designated registers of the power system, parse power operation response data, and complete plaintext transmission verification.

[0015] A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the multimodal large model incremental training method.

[0016] The technical effects achieved by this invention are as follows: This invention is a customized development for new power systems, adapting to the LoRa signal acquisition needs of multiple scenarios such as power distribution, power consumption, and new energy grid connection. By adjusting the sampling rate and center frequency, it achieves full coverage acquisition of LoRa signals in different scenarios and frequency bands of new power systems, breaking away from the scenario limitations of general detection technologies and solving the problem that existing technologies cannot adapt to the detection needs of new power systems.

[0017] This invention presents a custom-developed LoRa signal demodulation and decoding module. The demodulation algorithm is optimized for the CN470 standard and the transmission characteristics of LoRa signals in new power systems. Compared with general modules, it improves the accuracy and decoding efficiency of power message parsing. It can accurately identify new power system control messages or monitoring messages in the data payload, and solves the technical problem that existing general demodulation and decoding modules cannot accurately parse power messages.

[0018] This invention constructs a complete detection process of "signal acquisition - parameter cracking - customized decoding - power message parsing". By analyzing power operation data with a dedicated parsing tool, it can accurately verify the plaintext transmission characteristics of LoRa communication in new power systems, realize the rapid and accurate identification of plaintext transmission vulnerabilities, and achieve high detection efficiency and accurate results. It provides a direct and effective detection basis for the security protection of LoRa wireless communication in new power systems.

[0019] The detection method of this invention has clear steps and is highly operable. The detection system has a clear division of labor among its modules and efficient communication and coordination. It can be quickly deployed in LoRa communication scenarios in various aspects of the new power system, promptly detect plaintext transmission vulnerabilities, effectively prevent the leakage and malicious tampering of key data such as power monitoring data, control commands, and new energy grid connection parameters, and ensure the safe and stable operation of the new power system.

[0020] The detection system of this invention is not limited to specific software-defined wireless device models. The demodulation and decoding module is compatible with a full range of spreading factors from 6 to 12. It also supports multi-scenario adaptation for new power systems and has strong compatibility, scalability and adaptability. It can be applied to the detection of plaintext transmission vulnerabilities in LoRa wireless communication systems of new power systems with different parameter settings and application scenarios, and has a wide range of applications. Attached Figure Description

[0021] Figure 1 This is a flowchart of a wireless communication plaintext transmission vulnerability detection method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the connection between a software-defined wireless device and an industrial control computer, provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. This embodiment uses a LoRa industrial control communication system for a novel power system as the detection object. The novel power system here is characterized by the integration of new energy sources. The detection method and system of this invention will be described in detail. These embodiments are only for explaining this invention and are not intended to limit the scope of protection of this invention.

[0023] Example 1: Detection method for plaintext transmission vulnerabilities in LoRa wireless communication for new power systems: This embodiment provides a method for detecting plaintext transmission vulnerabilities in LoRa wireless communication in new power systems, adapted to detection scenarios on the grid-connected side of new energy sources. The specific steps are as follows: Step 1: Build a signal acquisition architecture that adapts to multiple scenarios and complete the target signal acquisition: Software-defined radio (SDR) devices were selected as the hardware acquisition end. A standardized signal acquisition architecture was built using the open-source software-defined radio framework (hereinafter referred to as GNURadio). The target frequency band of the SDR devices was initially set to the 470MHz range for acquisition adaptation in LoRa communication scenarios on the new energy grid-connected side. An FFT (Fast Fourier Transform) block was added to the GNURadio architecture to analyze signal components within the frequency range. Simultaneously, the maximum hold frame function of the FFT display was enabled to achieve real-time tracking of frequency components. The sampling rate (samp_rate) was adjusted to a suitable value to expand the frequency coverage. The center frequency was dynamically fine-tuned using the GNURadio slider. After triggering the signal from the LoRa communication system on the new energy grid-connected side, the acquisition of the target LoRa signal was completed. Step 2: Extract signal parameters and brute-force the spreading factor: Key parameters were extracted from the LoRa signal collected from the grid-connected new energy source. The center frequency of the signal was determined to be 471.5MHz and the bandwidth to be 250kHz through FFT analysis. For the standard range of LoRa spreading factor SF values ​​of 6, 7, 8, 9, 10, 11, and 12, a brute-force approach (i.e., traversal trial and error) was used to adapt the collected signals one by one. The adaptation result was judged by the signal demodulation success rate. Finally, the spreading factor of the target LoRa signal was determined to be 10. Step 3: Build a customized power system decoding and decoding architecture and complete data packet forwarding: A LoRa signal demodulation and decoding architecture is built based on a custom-developed LoRa signal demodulation and decoding module. This architecture is pre-configured with CSS linear frequency modulation spread spectrum and FEC forward error correction coding adaptation algorithms optimized for the CN470 standard and the LoRa signal transmission characteristics of the new power system's new energy grid connection side, improving the parsing accuracy of new energy grid connection parameter messages. The collected LoRaPHY signal is input into this LoRa signal demodulation and decoding architecture, and the determined signal parameters (center frequency 471.5MHz, bandwidth 250kHz, spreading factor 10) are imported to complete the demodulation and decoding of the signal. Through the module's built-in UDP forwarding function, the decoded data packets are forwarded to port 58868 of the local host in UDP protocol, keeping the port in listening state to facilitate subsequent packet capture and analysis of power messages. Step 4: Capture and analyze power message packets to identify plaintext transmission vulnerabilities: Open the Wireshark network packet capture tool and create a packet capture task targeting port 58868 on the local host to monitor UDP packets on this port in real time. After capturing the packets, parse the packet structure and find that the payload data segment in the packets contains control / monitoring messages from the new energy grid connection side (including photovoltaic grid-connected power, energy storage charging and discharging status, grid connection switch control commands, etc.). Write a power-specific parsing tool, import the packet capture data into the tool, and realize real-time parsing of power messages. By parsing the messages, send read / write commands to the corresponding registers of the Modbus slave address (address 01 in this case) contained therein, and receive and parse the new energy grid connection operation data replied by the registers. After parsing and verification, it was found that the key data of new energy grid connection was not encrypted and was transmitted directly in plaintext. Based on this, it was determined that there is a plaintext transmission vulnerability in the LoRa wireless communication of the new energy grid connection side of this new power system.

[0024] This method constructs a complete detection process of "signal acquisition - parameter cracking - customized decoding - power message parsing". By parsing power operation data with dedicated parsing tools, it can accurately verify the plaintext transmission characteristics of LoRa communication in new power systems, realize the rapid and accurate judgment of plaintext transmission vulnerabilities, and achieve high detection efficiency and accurate results. It provides a direct and effective detection basis for the security protection of LoRa wireless communication in new power systems. Furthermore, the method and steps are clear and highly operable, and can be quickly deployed in LoRa communication scenarios in all aspects of the new power system. It can promptly detect plaintext transmission vulnerabilities, effectively prevent the leakage and malicious tampering of key data such as power monitoring data, control commands, and new energy grid connection parameters, and ensure the safe and stable operation of the new power system.

[0025] Implementation 2: A detection system for plaintext transmission vulnerabilities in LoRa wireless communication for new power systems. This embodiment provides a detection system for plaintext transmission vulnerabilities in LoRa wireless communication in new power systems, implementing the detection method of Embodiment 1. It is adapted to new energy grid-connected scenarios and includes a signal acquisition module, a parameter analysis module, a demodulation and decoding module, and a message parsing module. These modules are interconnected via a data interface and work together to acquire, analyze, demodulate, decode, and detect plaintext transmission vulnerabilities in LoRa signals for new power systems. The hardware and software configuration and workflow are as follows: (1) Signal acquisition module: Hardware configuration: Software-defined wireless equipment, industrial control computer (running Ubuntu system, supporting GNURadio, adapted to power field industrial environment); Software configuration: GNURadio, FFT analysis plugin, sampling rate / center frequency adjustment plugin, and new power system multi-scenario acquisition adaptation plugin; Workflow: The industrial control computer runs GNURadio to build a signal acquisition architecture. The new power system multi-scenario acquisition and adaptation plugin completes the adaptation of the new energy grid-connected side scenario. The software-defined wireless device acquires LoRa signals in the 470MHz range and transmits them to the industrial control computer. The FFT analysis plugin analyzes the signal frequency components, the maximum hold-frame plugin tracks the signal characteristics, the sampling rate adjustment plugin adjusts the samp_rate to expand the frequency coverage range, and the center frequency adjustment plugin dynamically adjusts the center frequency through the slider of GNURadio. Finally, the acquired new energy grid-connected target LoRa signal is transmitted to the parameter analysis module. By adjusting the sampling rate and center frequency, full coverage acquisition of LoRa signals in different scenarios and frequency bands of the new power system is achieved, thus overcoming the scenario limitations of general detection technologies.

[0026] (2) Parameter Analysis Module: Operating platform: Shares the same industrial control computer as the signal acquisition module; Software configuration: LoRa signal parameter extraction program for new power systems, brute-force cracking program for spreading factor; Workflow: The LoRa signal parameter extraction program for the new power system automatically extracts and saves the center frequency and bandwidth parameters from the received LoRa signal from the grid-connected new energy source; the brute-force cracking program for the spreading factor is adapted and demodulated one by one for the acquired signals with spreading factor values ​​of 6-12, and automatically determines the spreading factor to be 10 based on the demodulation success rate; after standardizing and encapsulating all signal parameters (center frequency 471.5MHz, bandwidth 250kHz, spreading factor 10), they are transmitted to the demodulation and decoding module.

[0027] (3) Demodulation / decoding module: Operating platform: Shares the same industrial control computer as the signal acquisition module; Software configuration: Custom-developed LoRa signal demodulation and decoding module, new power system LoRaPHY signal demodulation and decoding program, UDP packet forwarding program; Workflow: The new power system LoRaPHY signal demodulation and decoding program imports the new power system LoRa signal transmission characteristic optimization demodulation algorithm from the LoRa signal demodulation and decoding module, receives signal parameters transmitted by the parameter analysis module and completes the configuration, demodulates and decodes the input LoRaPHY signal, and accurately analyzes the new energy grid connection related messages; the UDP packet forwarding program forwards the decoded data packets to the local host port 58868 in real time in the form of UDP protocol, and generates a power message forwarding log at the same time; Compared to general modules, it improves the accuracy and decoding efficiency of power message parsing, and can accurately identify new power system control messages or monitoring messages contained in the data payload; the LoRa signal demodulation and decoding module is compatible with a full range of spreading factors of 6-12, and supports multi-scenario adaptation of new power systems, with strong compatibility, scalability and adaptability.

[0028] (4) Message parsing module: Operating platform: Shares the same industrial control computer as the signal acquisition module; Software configuration: Wireshark packet capture software, Python 3.8 or above, Modbus power message parsing script for new power systems, and power system register instruction interaction script; Workflow: Wireshark packet capture software monitors UDP packets on port 58868, captures the data, saves it in pcap format, and transmits it to the Python parsing environment; the new power system Modbus power message parsing script parses the captured data, identifies the new energy grid-connected control / monitoring messages in the payload segment; the power system register instruction interaction script sends standard instructions to a specific register containing the Modbus slave address (address 01 in this case), receives the new energy grid-connected operation data replied by the register, and performs plaintext verification; if the verification result is plaintext transmission, the script automatically outputs the detection result "plaintext transmission vulnerability exists", and generates a detailed power message parsing and vulnerability detection report, including vulnerability location, leaked data type, risk level, and other information.

[0029] This invention addresses the security issues of data leakage and tampering in power monitoring and control systems caused by plaintext transmission in LoRa wireless communication in new power systems. It can be rapidly deployed in LoRa communication scenarios such as the distribution side, consumption side, and new energy grid connection side of new power systems, providing accurate detection basis for wireless communication security protection in new power systems and effectively preventing the risk of leakage and malicious attacks on critical power data.

[0030] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for novel power systems, characterized in that, The specific steps are as follows: Multi-scenario signal acquisition stage: A signal acquisition architecture is built using software-defined wireless equipment combined with GNURadio. The signal components within the frequency range are analyzed by FFT fast Fourier transform blocks. The maximum holding frame of the FFT display is enabled to track the frequency components and capture the target LoRa signal. Signal parameter extraction and brute-force cracking stage: Extract the center frequency and bandwidth parameters from the captured target LoRa signal of the new power system. For the LoRa spreading factor SF values ​​of 6, 7, 8, 9, 10, 11, and 12, use a brute-force cracking method to determine the spreading factor of the target LoRa signal. Customized demodulation and decoding and data packet forwarding stage: Based on the custom-developed LoRa signal demodulation and decoding module, a LoRa signal demodulation and decoding architecture is built. The LoRaPHY signal collected in the multi-scenario signal acquisition stage is input to the LoRa signal demodulation and decoding architecture to complete signal demodulation and decoding. The decoded data packet is forwarded to port 58868 of the local host in UDP form. Power Message Parsing and Vulnerability Assessment Phase: UDP packets on port 58868 are monitored using Wireshark network packet capture tool. The message content in the packets is parsed to detect whether the packet payload segment contains new power system control / monitoring messages. If so, read / write commands are sent to the corresponding register of the Modbus slave address contained therein. The power operation data replied by the register is obtained and parsed to verify whether the data is transmitted in plaintext. If it is transmitted in plaintext, it is determined that the LoRa wireless communication of the new power system has a plaintext transmission vulnerability.

2. The method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for new power systems according to claim 1, characterized in that: In the multi-scenario signal acquisition stage, the target frequency band of the software-defined wireless device is set to the 470MHz range to adapt to the LoRa communication scenario of a new power system characterized by new energy access. The frequency coverage can be expanded by adjusting the sampling rate (samp_rate), and the center frequency can be dynamically changed using the slider in GNURadio.

3. The method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for new power systems according to claim 1, characterized in that: The target LoRa signal extracted during the signal parameter extraction and brute-force attack phase has a center frequency of 471.5MHz and a bandwidth of 250kHz. The spreading factor determined after the brute-force attack is 10.

4. The method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for new power systems according to claim 1, characterized in that: The LoRa signal demodulation and decoding module in the customized demodulation and decoding and data packet forwarding stages is based on the characteristics of CSS linear frequency modulation spread spectrum and FEC forward error correction coding, and optimizes the demodulation algorithm for the LoRa signal transmission characteristics of CN470 standard and new power system.

5. The method for detecting plaintext transmission vulnerabilities in LoRa wireless communication for new power systems according to claim 1, characterized in that: In the power message parsing and vulnerability assessment phase, the Modbus slave address is address 01. A dedicated parsing tool is written to parse the new power system control / monitoring messages mixed in the message in real time, and to perform operations such as sending power system register instructions and parsing power operation reply data.

6. A detection system for LoRa wireless communication plaintext transmission vulnerabilities in novel power systems, used to implement the LoRa wireless communication plaintext transmission vulnerability detection method according to any one of claims 1-5, characterized in that: It includes a signal acquisition module, a parameter analysis module, a demodulation and decoding module, and a message parsing module. The modules are interconnected and adapted to LoRa communication vulnerability detection in multiple scenarios of new power systems. The signal acquisition module consists of a software-defined wireless device and a signal acquisition architecture built with GNURadio. It is used to acquire LoRa signals in the 470MHz range of the new power system, and is adapted to the acquisition scenarios of power distribution, power consumption, and new energy grid connection. It analyzes frequency components through FFT blocks and enables maximum hold frame tracking of signal characteristics. The parameter analysis module is used to extract the center frequency and bandwidth parameters from the target LoRa signal of the new power system, and to determine the spreading factor through brute-force cracking. The demodulation and decoding module is built on a custom-developed LoRa signal demodulation and decoding module. It is used to demodulate and decode the LoRaPHY signal according to the determined signal parameters and forward the decoded UDP data packets to port 58868 of the local host. The message parsing module is used to monitor UDP packets on port 58868, parse the relevant message content of the power system and verify whether the data is transmitted in plaintext, and determine whether there is a plaintext transmission vulnerability in the new power system LoRa wireless communication.

7. The detection system for plaintext transmission vulnerabilities in LoRa wireless communication for novel power systems according to claim 6, characterized in that: The signal acquisition module is equipped with a sampling rate adjustment unit and a center frequency adjustment unit. The sampling rate adjustment unit is used to adjust the sample rate to expand the frequency coverage range, and the center frequency adjustment unit realizes dynamic adjustment of the center frequency through the GNURadio slider to adapt to the LoRa signal acquisition needs of different scenarios in new power systems.

8. The detection system for plaintext transmission vulnerabilities in LoRa wireless communication for novel power systems according to claim 6, characterized in that: The demodulation and decoding module supports LoRa proprietary spread spectrum methods derived from CSS linear frequency modulation spread spectrum and FEC forward error correction coding, and is compatible with spread spectrum factor demodulation and decoding of 6-12. The LoRa signal demodulation and decoding module optimizes demodulation accuracy and decoding efficiency for the CN470 standard and the transmission characteristics of LoRa signals in new power systems, and is compatible with power system message parsing.

9. The detection system for plaintext transmission vulnerabilities in LoRa wireless communication for new power systems according to claim 6, characterized in that: The message parsing module includes a Wireshark packet capture module and a power system-specific Python data parsing submodule; the Wireshark packet capture module is used to monitor UDP packets on port 58868. The dedicated Python data parsing submodule for power systems has a built-in power message parsing script, which is used to parse control / monitoring messages in the message payload segment in real time, send instructions to designated registers of the power system, parse power operation response data, and complete plaintext transmission verification.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the LoRa wireless communication plaintext transmission vulnerability detection method as described in any one of claims 1 to 5.

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