Digital power grid network attack and defense confrontation technology benchmarking analysis method

Abstract

The invention discloses a digital power grid network attack and defense confrontation technology benchmarking analysis method, which comprises the following steps of: 1, acquiring external data of each key business scene of a digital power grid, including intelligent scheduling, power distribution automation, power utilization information acquisition and a network interaction and access communication relationship of distributed energy access, identifying vulnerabilities and attack paths of each service link; 2, analyzing the countermeasure model and the framework technology of the digital power grid network attack and defense, performing benchmarking analysis on the countermeasure technology of the digital power grid network attack and defense, and outputting a technical solution of the countermeasure model and the framework of the digital power grid network attack and defense; the technical problems that in the prior art, network security protection still has obvious defects in the aspect of adapting to the digital power grid and cannot effectively face the digital power grid to defend network attacks under extreme conditions are solved.

Description

Technical Field

[0001] The present invention belongs to the field of network security technology, and in particular relates to a benchmarking analysis method for digital power grid network attack and defense countermeasure technologies. Background Art

[0002] Amidst the digital transformation of the energy industry, digital grids, as the core vehicle for next-generation power systems, face increasingly severe cybersecurity challenges. Currently, digital grid systems feature advanced intelligence, wide-area interconnectivity, and data integration. While this open architecture improves operational efficiency, it also presents more complex cybersecurity threats. International cybersecurity incidents have demonstrated that emerging attack methods, such as advanced persistent threats (APTs) and ransomware attacks targeting critical information infrastructure, pose a real threat to the safe and stable operation of power systems.

[0003] Existing technologies have conducted a series of research in the field of digital security, mainly focusing on the analysis of attack and defense technology mechanisms and the construction of dynamic protection models. However, existing research results still have obvious deficiencies in adapting to digital power grids and cannot effectively resist network attacks in extreme situations. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a benchmarking and analysis method for digital power grid network attack and defense countermeasures technologies, so as to solve the technical problems that the network security protection of the existing technology still has obvious deficiencies in adapting to the digital power grid and cannot effectively deal with the digital power grid in extreme situations.

[0005] The technical solution of the present invention is: A digital power grid network attack and defense technology benchmarking analysis method, the method comprising: Step 1: Collect external data for key business scenarios of the digital grid, including network interactions and access connections for intelligent scheduling, distribution automation, electricity consumption information collection, and distributed energy access, to identify vulnerabilities and attack paths in each business link. Step 2: Analyze the adversarial model and framework technology of digital power grid network attack and defense, conduct benchmarking analysis of digital power grid network attack and defense adversarial technology, and output technical solutions for the adversarial model and framework of digital power grid network attack and defense.

[0006] The implementation method of step 1 includes: combining the network interaction characteristics and security requirements of the business scenario, and selecting threat-based attack surface modeling, attack surface-based attack surface modeling, and layered attack surface modeling.

[0007] The threat-based attack surface modeling method includes: starting from known threat intelligence, by analyzing historical attack events, vulnerability exploitation techniques and attacker tactics, reversely deducing the attack paths that the digital power grid may face.

[0008] Layered attack surface modeling includes: analyzing the vulnerabilities and attack points within each layer layer by layer according to the layered logic of "physical layer-network layer-system layer-application layer" or "terminal-regional boundary-communication network-cloud platform", and strengthening the encryption and authentication of power-specific protocols at the network layer.

[0009] Attack surface modeling based on the attack surface includes: starting from the actual business interactions and network structure of the digital power grid, systematically identifying and quantifying attack entry points, and constructing an attack surface map covering the entire "end-edge-cloud" scenario by sorting out the access control policies and potential vulnerabilities of smart meters, substation monitoring systems and cloud platform nodes.

[0010] Attack surface-based modeling is combined with the three-dimensional perspective of "business-network-data" of the digital power grid. First, through asset mapping and traffic analysis, a data flow diagram of the key power grid businesses is drawn to clarify the interactive relationships between each subdomain. Secondly, vulnerability scanning and penetration testing are combined to quantify the attack exposure of the Internet boundary and vertical through-links. Finally, security policies are dynamically adjusted based on the attack surface heat map.

[0011] Analysis of the adversarial models and framework technologies for digital power grid network attack and defense includes: using a threat hunting system driven by the ATT&CK framework, mapping the power grid business system to the ATT&CK matrix, establishing a detection rule library covering the entire attack life cycle, optimizing the TTPs tactics, techniques and processes of the energy industry, and building a threat detection and response capability covering all business scenarios of the digital power grid; building a dynamic defense system based on zero trust to address the risk of lateral movement within the power grid system, and reshaping the traditional boundary protection model through continuous identity authentication, micro-isolation and least privilege access control; building an AI-enabled intelligent attack and defense deduction platform, using machine learning algorithms to analyze historical attack and defense data, simulate attacker behavior patterns, and provide decision support for defense strategy optimization.

[0012] The threat hunting system driven by the ATT&CK framework includes attack behavior modeling, detection capability building, and response mechanism optimization. The attack behavior modeling extracts attack techniques targeting the energy industry through analysis of historical security incidents, including lateral movement using the OPC interface and denial-of-service attacks against SCADA systems. Detection capability building derives multi-dimensional detection rules adapted to power grid services. Response mechanism optimization improves handling efficiency and establishes a closed-loop processing process from the security operation center to on-site operation and maintenance to ensure that the impact can be quickly contained after an attack is discovered.

[0013] The zero-trust dynamic defense system addresses the risk of lateral movement within the digital power grid through fine-grained access control and continuous identity authentication, deploys identity-based access agents and micro-segmentation policies to ensure that each access request is strictly verified.

[0014] The AI-enabled intelligent attack and defense simulation platform realizes the simulation of attack and defense scenarios by building a digital twin environment, helping security teams predict potential risks and verify the effectiveness of protective measures.

[0015] The beneficial effects of the present invention are: The present invention organizes external data on the attack surface of key business scenarios of the digital power grid, combines the current status and development trends of the digital power grid, collects data on key business scenarios of the digital power grid, network interactions and access connectivity relationships of key businesses, and combines the characteristics of the internal attack surface and exposure impact of the new digital power grid key business scenarios, network subdomain boundaries, network vertical and horizontal regional boundaries and Internet boundaries. Through a comprehensive review of key business scenarios, the vulnerabilities and attack paths of each business link are identified.

[0016] The present invention conducts a differentiated comparative analysis of the adversarial models and framework technologies for digital power grid network attack and defense, organizes and outputs technical solutions for the adversarial models and frameworks for digital power grid network attack and defense, and then designs a new generation of digital power grid network security protection models and frameworks, thereby improving the ability of digital power grids to resist network attacks in extreme situations.

[0017] It solves the technical problems that the network security protection of existing technologies is still obviously insufficient in adapting to digital power grids and cannot effectively deal with digital power grids resisting network attacks in extreme situations. DETAILED DESCRIPTION

[0018] A digital power grid network attack and defense technology benchmarking analysis method, including: Step 1: Collect external data on the attack surface of key business scenarios of the digital power grid: This invention collects external data from key business scenarios in digital power grids, including network interactions and access connections for core businesses such as intelligent dispatching, distribution automation, electricity consumption data collection, and distributed energy access. Combining the current state of digital power grids with the development trends of intelligent, distributed, and interactive approaches, this method conducts an in-depth analysis of the evolving network attack surface in these new business scenarios, focusing on attack exposure points and potential impacts within internal systems, network subdomain boundaries, vertical and horizontal regional boundaries, and internet boundaries. Through a comprehensive review of key business scenarios, vulnerabilities and attack paths within each business link are identified.

[0019] In the attack surface modeling of the digital power grid, the present invention combines the network interaction characteristics and security requirements of its key business scenarios, and the selected modeling schemes include threat-based attack surface modeling, attack surface-based attack surface modeling, and layered attack surface modeling.

[0020] Threat-based modeling of the digital power grid attack surface: The core concept is to reverse engineer potential attack paths facing digital grids by analyzing known threat intelligence, historical attack events, vulnerability exploitation techniques, and attacker tactics (e.g., attack patterns used by APT groups targeting the energy industry). This approach offers the advantage of directly linking to real-world threats, such as malware targeting grid industrial control systems (e.g., Industroyer), supply chain attacks (e.g., risks stemming from the SolarWinds incident), or phishing penetration techniques, allowing for rapid identification of high-risk nodes. However, its limitation lies in its over-reliance on the completeness of external threat data. Digital grid business architectures have distinct industry characteristics (e.g., specific communication protocols and hierarchical scheduling systems), and generic threat intelligence may not cover their entire attack surface. For example, risks such as wireless private network interference attacks in distribution automation systems and smart meter firmware tampering may be overlooked by the model if not captured in public threat libraries. Furthermore, threat-driven modeling often lags behind the evolution of new attack techniques, making it difficult to address zero-day vulnerabilities or customized attacks emerging during the digital transformation of power grids.

[0021] Layered digital power grid attack surface modeling: Taking a hierarchical approach to network defense, following a layered logic such as "physical layer - network layer - system layer - application layer" or "terminal - regional boundary - communication network - cloud platform," vulnerabilities and attack points within each layer are analyzed step by step. This approach aligns with the traditional defense-in-depth philosophy of cybersecurity and clearly delineates defense responsibilities. For example, at the physical layer, the focus is on preventing device side-channel attacks, while at the network layer, encryption and authentication for power-specific protocols are strengthened. However, digital power grid business flows often span multiple layers (e.g., dispatch instructions are sent from the cloud platform to the terminal device via the communication network). Layered modeling can fragment the overall risk picture of the business chain. For example, analyzing API abuse risks in "cloud-edge collaboration" scenarios independently at the cloud platform or edge device layer can overlook cross-layer attack chains (e.g., exploiting edge device vulnerabilities to jump to the cloud platform). Furthermore, layered models lack adaptability to new converged attacks (e.g., exploiting 5G network slicing vulnerabilities to penetrate production control zones) and fail to dynamically reflect the evolving attack surface.

[0022] Attack surface-based digital power grid attack surface modeling: The core of attack surface-based digital power grid attack surface modeling is to systematically identify and quantify attack entry points based on the actual business interactions and network structure of the digital power grid, rather than relying solely on known threats or layered defense perspectives, so as to better meet the actual security needs of power grid business.

[0023] Given the complexity and dynamic nature of digital grid services, key digital grid services (such as dispatching and control, and electricity consumption data collection) involve extensive cross-domain data interaction. Traditional threat-based modeling may be limited by the scope of threat intelligence and fail to fully capture the unique attack paths of the power grid. While layered modeling provides a clear delineation of defense layers, it may overlook the overall risk of the service chain. In contrast, attack surface-based modeling directly focuses on the network access relationships, service data flows, and exposed interfaces of the power grid. By analyzing access control policies and potential vulnerabilities at key nodes such as smart meters, substation monitoring systems, and cloud platforms, a comprehensive attack surface map covering the entire "end-edge-cloud" scenario is constructed. For example, in distribution automation systems, attack surface modeling can accurately identify communication protocol vulnerabilities between the SCADA system and distribution terminals, exposed access risks exposed by wireless private networks, and potential interception points for cross-provincial data transmission, enabling the development of targeted access control and intrusion detection strategies.

[0024] Attack surface modeling will be implemented from a three-dimensional perspective: "business-network-data" for the digital grid. First, through asset mapping and traffic analysis, a data flow diagram for key grid services will be constructed, clarifying the interactions between subdomains (such as the production control area and the management information area). Second, vulnerability scanning and penetration testing will be combined to quantify the attack exposure of the internet perimeter and vertical interconnected links (such as the four-tier network level: provincial, municipal, and county). Finally, based on the attack surface heat map, security policies will be dynamically adjusted. For example, protocols such as protocol whitelisting will be implemented in the centralized smart meter access area and baseline monitoring of API access behavior will be implemented on the dispatching cloud platform. This solution is superior in its ability to directly map real business risks, effectively supporting the security protection needs of the digital grid in the construction of new power systems. It also provides a practical data foundation for subsequent threat hunting and attack and defense drills. Future collaboration with the ATT&CK threat framework will further optimize the dynamic attack surface assessment mechanism and achieve closed-loop management of digital grid security.

[0025] Step 2: Analyze the adversarial models and framework technologies for digital power grid network attack and defense, and conduct a benchmark analysis of digital power grid network attack and defense adversarial technologies.

[0026] This invention sorts out the adversarial model and framework technology and conducts differentiated comparative analysis, conducts technical verification of the adversarial model and framework for digital power grid network attack and defense against the network architecture of power grid companies, provinces, cities, counties and large collective enterprises, and organizes and outputs technical solutions for the adversarial model and framework for digital power grid network attack and defense.

[0027] This paper benchmarks and analyzes network attack and defense countermeasure technologies, combing through ATT&CK, Cyber ​​Kill Chain, and NISTCSF attack and defense countermeasure models and frameworks. A differentiated comparative analysis is conducted based on applicable scenarios, technical features, and implementation costs. Based on the complex four-tiered architecture of power grid companies (provinces, cities, and counties) and the network characteristics of large collective enterprises, the adaptability of attack and defense countermeasure technologies is verified in typical digital power grid business scenarios. By simulating each stage of the attack chain, the effectiveness of different countermeasure models in terms of detection accuracy, response speed, and protection coverage is tested. The feasibility of cutting-edge technologies such as zero-trust micro-segmentation, threat intelligence-driven collaborative defense, and AI-assisted anomaly detection in digital power grid environments is emphasized. Ultimately, a technical solution is integrated to cover the entire lifecycle of attack prevention, continuous monitoring, real-time response, and rapid recovery. This solution should inherit the proven methodologies of traditional network security frameworks while being specifically optimized for the unique business continuity requirements, wide-area distribution characteristics, and industrial control protocol vulnerabilities of digital power grids. This approach deeply integrates attack and defense countermeasure capabilities with power grid business needs, providing theoretical support and practical paths for building a dynamic defense system for digital power grids.

[0028] In building a digital power grid network attack and defense countermeasure system, three representative technical approaches were developed, targeting the complex four-tiered architecture of China Southern Power Grid (CSG) across provinces, prefectures, and counties, as well as the characteristics of its large collective enterprise network. Solution 1 utilizes a threat hunting system driven by the ATT&CK framework. By mapping power grid business systems to the ATT&CK matrix, this solution establishes a detection rule base covering the entire attack lifecycle, specifically optimizing TTPs (tactics, techniques, and procedures) commonly encountered in the energy industry. Solution 2 builds a dynamic defense system based on zero trust, focusing on addressing the risk of lateral movement within the power grid system. Through continuous identity authentication, micro-segmentation, and least privilege access control, it reshapes the traditional perimeter protection model. Solution 3 designs an AI-powered intelligent attack and defense simulation platform, leveraging machine learning algorithms to analyze historical attack and defense data, simulate attacker behavior patterns, and provide decision support for optimizing defense strategies. These three solutions provide a comprehensive technical perspective on digital power grid attack and defense countermeasures from the perspectives of threat awareness, defense architecture, and intelligent decision-making.

[0029] Zero Trust Dynamic Defense System: This solution reshapes the traditional network security perimeter with the core principle of "never trust, continuously verify." Through fine-grained access control and continuous identity authentication, it effectively addresses the increasingly complex lateral movement risks within the digital power grid. Technically, it prioritizes the deployment of identity-based access proxies and micro-segmentation strategies to ensure that every access request is rigorously verified.

[0030] AI-enabled intelligent attack and defense simulation platform: Leveraging machine learning algorithms to analyze massive amounts of security data, simulate attacker behavior patterns, and provide decision support for optimizing defense strategies. This solution builds a digital twin environment to simulate attack and defense scenarios, helping security teams anticipate potential risks and verify the effectiveness of protective measures.

[0031] Threat hunting system driven by the ATT&CK framework: The threat hunting system driven by the ATT&CK framework focuses on analyzing the attack surface in the "cloud-edge-end" collaborative environment of the China Southern Power Grid, such as the API interface of the dispatching cloud platform and the physical interface of the substation edge equipment, to form an attack technique and tactics library with China Southern Power Grid characteristics.

[0032] This solution is centered on the MITRE ATT&CK knowledge base, and by systematically sorting out attacker tactics, techniques, and procedures (TTPs), it builds threat detection and response capabilities covering all business scenarios of the digital power grid.

[0033] This solution demonstrates unique advantages within the complex four-tier architecture of the power grid, encompassing provinces, cities, and counties. First, it provides a standardized attack behavior description framework that can link scattered security incidents into a complete attack chain. Second, its rich technical matrix is ​​particularly well-suited for the power industry's unique industrial control protocols and business systems, such as specialized detection rules for protocols like IEC 61850 and DNP3. Most importantly, the framework supports continuous evolution and can dynamically incorporate new attack methods to maintain the timeliness of the defense system.

[0034] In terms of specific implementation, Plan 1 focuses on three key areas: attack behavior modeling, detection capability development, and response mechanism optimization. During the attack behavior modeling phase, analysis of historical security incidents at the China Southern Power Grid identified 23 key attack techniques targeting the energy industry, including lateral movement exploiting OPC interfaces and denial-of-service attacks against SCADA systems. Regarding detection capabilities, multi-dimensional detection rules tailored to power grid operations were developed. For example, a composite detection model based on network traffic and logs was deployed in the dispatching system to enable early detection of APT attacks. Response mechanism optimization focused on improving response efficiency, establishing a closed-loop process from the security operations center to on-site operations and maintenance to ensure rapid containment of attacks once discovered. In pilot deployments, this solution successfully reduced the average detection time for advanced threats from 72 hours to 4 hours and reduced false alarm rates by 60%, fully demonstrating its applicability in power grid environments.

Claims

1. A digital power grid network attack and defense technology benchmarking analysis method, characterized by: The method comprises: Step 1: Collect external data for key business scenarios of the digital grid, including network interactions and access connections for intelligent scheduling, distribution automation, electricity consumption information collection, and distributed energy access, to identify vulnerabilities and attack paths in each business link. Step 2: Analyze the adversarial model and framework technology of digital power grid network attack and defense, conduct benchmarking analysis of digital power grid network attack and defense adversarial technology, and output technical solutions for the adversarial model and framework of digital power grid network attack and defense.

2. The digital power grid network attack and defense technology benchmarking analysis method according to claim 1 is characterized by: The implementation method of step 1 includes: combining the network interaction characteristics and security requirements of the business scenario, and selecting threat-based attack surface modeling, attack surface-based attack surface modeling, and layered attack surface modeling.

3. The digital power grid network attack and defense technology benchmarking analysis method according to claim 2 is characterized by: The threat-based attack surface modeling method includes: starting from known threat intelligence, by analyzing historical attack events, vulnerability exploitation techniques and attacker tactics, reversely deducing the attack paths that the digital power grid may face.

4. The digital power grid network attack and defense technology benchmarking and analysis method according to claim 2 is characterized by: Layered attack surface modeling includes: analyzing vulnerabilities and attack points within each layer layer by layer according to the layered logic of "physical layer-network layer-system layer-application layer" or "terminal-regional boundary-communication network-cloud platform", and strengthening the encryption and authentication of power-specific protocols at the network layer.

5. The digital power grid network attack and defense technology benchmarking analysis method according to claim 2 is characterized by: Attack surface modeling based on the attack surface includes: starting from the actual business interactions and network structure of the digital power grid, systematically identifying and quantifying attack entry points, and constructing an attack surface map covering the entire "end-edge-cloud" scenario by sorting out the access control policies and potential vulnerabilities of smart meters, substation monitoring systems, and cloud platform nodes.

6. The digital power grid network attack and defense technology benchmarking analysis method according to claim 5 is characterized by: Attack surface modeling is combined with the three-dimensional perspective of "business-network-data" for the digital power grid. First, through asset mapping and traffic analysis, a data flow diagram for key grid businesses is created to clarify the interactions between subdomains. Secondly, vulnerability scanning and penetration testing are combined to quantify the attack exposure of the Internet boundary and vertical links. Finally, security policies are dynamically adjusted based on the attack surface heat map.

7. The digital power grid network attack and defense technology benchmarking analysis method according to claim 1 is characterized by: Analysis of the adversarial models and framework technologies for digital power grid network attack and defense includes: using a threat hunting system driven by the ATT&CK framework, mapping the power grid business system to the ATT&CK matrix, establishing a detection rule library covering the entire attack life cycle, optimizing the TTPs tactics, techniques and processes of the energy industry, and building a threat detection and response capability covering all business scenarios of the digital power grid; building a dynamic defense system based on zero trust to address the risk of lateral movement within the power grid system, and reshaping the traditional boundary protection model through continuous identity authentication, micro-isolation and least privilege access control; building an AI-enabled intelligent attack and defense deduction platform, using machine learning algorithms to analyze historical attack and defense data, simulate attacker behavior patterns, and provide decision support for defense strategy optimization.

8. The digital power grid network attack and defense technology benchmarking and analysis method according to claim 7 is characterized by: The threat hunting system driven by the ATT&CK framework includes attack behavior modeling, detection capability building, and response mechanism optimization. The attack behavior modeling extracts attack techniques targeting the energy industry through analysis of historical security incidents, including lateral movement using the OPC interface and denial-of-service attacks against SCADA systems. Detection capability building derives multi-dimensional detection rules adapted to power grid services. Response mechanism optimization improves handling efficiency and establishes a closed-loop processing process from the security operation center to on-site operation and maintenance to ensure that the impact can be quickly contained after an attack is discovered.

9. The digital power grid network attack and defense technology benchmarking analysis method according to claim 7 is characterized by: The zero-trust dynamic defense system addresses the risk of lateral movement within the digital power grid through fine-grained access control and continuous identity authentication, deploys identity-based access agents and micro-segmentation policies to ensure that each access request is strictly verified.

10. The digital power grid network attack and defense technology benchmarking and analysis method according to claim 7 is characterized by: The AI-enabled intelligent attack and defense simulation platform realizes the simulation of attack and defense scenarios by building a digital twin environment, helping security teams predict potential risks and verify the effectiveness of protective measures.