Information Security in Power Monitoring Systems: Technologies and Applications

Power monitoring systems undertake the core tasks of real-time grid monitoring, fault diagnosis, and operational optimization. Their security directly affects the stability and reliability of power systems. With the deepening application of technologies such as cloud computing, the Internet of Things (IoT), and big data in the power industry, information security risks for power monitoring systems are gradually increasing.

These systems face multiple challenges, including advanced persistent threats (APT), denial-of-service (DoS) attacks, and malware infections. Traditional security architectures rely on single-layered defense strategies, which are difficult to effectively counter complex attack methods. It is necessary to adopt a defense-in-depth architecture and enhance the system’s anti-attack capabilities through multi-layered security mechanisms.

1. Composition and Functions of Power Monitoring Systems

A power monitoring system is a comprehensive power automation management platform primarily used for real-time monitoring, control, and optimization of the operational status of power systems. The system typically consists of a monitoring center, data acquisition and transmission devices, intelligent terminals, communication networks, and application software. The monitoring center, serving as the core hub, is responsible for processing massive amounts of power data, analyzing operational status, and executing control commands.

Data acquisition devices, such as Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs), obtain key parameters such as current, voltage, and frequency through sensors and communication interfaces, and transmit the data to the main control system. Communication networks typically use protocols such as IEC 61850, DNP3, and Modbus to ensure the efficiency and reliability of data transmission.

Application software includes functions such as dispatch management, load forecasting, state estimation, and fault diagnosis, supporting grid operation optimization and early warning of abnormal conditions. Modern power monitoring systems have widely adopted cloud computing, edge computing, and artificial intelligence (AI) technologies to improve data processing capabilities and decision-making efficiency. The system involves power dispatching, equipment control, and data analysis, and its security directly relates to grid stability and national energy security.

2. Information Security Protection System of Power Monitoring Systems

2.1 Network Security Protection Strategy

The network security protection strategy for power monitoring systems needs to build a defense-in-depth system from multiple levels, including physical isolation, protocol security, traffic monitoring, and active defense, to effectively address risks of malicious attacks and data theft. First, regarding the network architecture of power monitoring systems, a network zoning strategy should be adopted to physically or logically isolate the control network, management network, and office network to reduce the attack surface, and unidirectional data flow technology should be used to ensure that core control signals cannot be tampered with.

Second, for communication protocol security, encrypted tunnel technologies (such as TLS 1.3) should be used to protect the data transmission security of critical protocols such as IEC 61850 and DNP3, and MACsec (IEEE 802.1AE) should be introduced to provide link-layer encryption, preventing man-in-the-middle attacks and data hijacking. In terms of traffic monitoring, an AI-based abnormal traffic detection system (AI-IDS) should be deployed, using deep learning algorithms to analyze packet characteristics and detect abnormal behaviors, improving detection accuracy to over 99%.

At the same time, combined with a DDoS protection system, through rate limiting and automatic failover mechanisms, the impact of traffic attacks on power dispatch centers can be reduced. Finally, in terms of active defense, a Zero Trust Architecture (ZTA) can be adopted to continuously authenticate and control access for all traffic, preventing the spread of internal threats, thereby enhancing the network security of power monitoring systems.

2.2 Identity Authentication and Access Control

The identity authentication and access control system of power monitoring systems must ensure the legitimacy of users, devices, and applications, preventing unauthorized access and privilege abuse. On one hand, in terms of identity authentication, a digital certificate authentication mechanism based on Public Key Infrastructure (PKI) should be adopted, assigning unique identity identifiers to operation and maintenance personnel, SCADA system components, and intelligent terminal devices.

Through two-factor authentication (2FA), one-time passwords (OTP), and biometric identification technologies (such as fingerprint or iris recognition), the security of identity verification can be enhanced. In remote access scenarios, the FIDO2 protocol can be adopted to support passwordless authentication, reducing the risk of credential theft. On the other hand, in terms of access control, a combined mechanism of Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) should be implemented to ensure that user permissions strictly match their responsibilities, preventing unauthorized access.

For example, substation operation and maintenance personnel can only access specific equipment, while dispatchers are limited to data monitoring and command issuance. To further refine access strategies, dynamic permission adjustment mechanisms can be adopted, adjusting access permissions in real time based on user behavior patterns and environmental variables (such as geographical location, device type, etc.). An access log auditing system (SIEM) should be used to record all access requests and combine machine learning techniques to analyze abnormal access behaviors, improving the detection capability of internal security threats, ensuring the secure and stable operation of power monitoring systems.

2.3 Data Security and Encryption Technologies

The data security of power monitoring systems involves stages such as data storage, transmission, processing, and backup. High-strength encryption algorithms and access control mechanisms must be adopted to ensure data confidentiality, integrity, and availability.

First, in the data storage phase, AES-256 should be used to encrypt sensitive data at rest, and Shamir's Secret Sharing (SSS) should be combined to split and store keys, preventing single-point leakage. Second, in the data transmission process, the TLS 1.3 protocol should be used to perform end-to-end encryption for communication between SCADA systems and intelligent terminals, and Elliptic Curve Cryptography (ECC) should be adopted to improve encryption efficiency and reduce computational resource consumption.

Finally, to ensure data integrity, the hash function SHA-512 should be used to generate hash values, and HMAC should be combined for data verification to prevent tampering attacks. For data storage security, an immutable log storage technology based on blockchain can be applied, using smart contracts to automatically enforce access control and improve data credibility. In terms of data backup, the 3-2-1 strategy should be adopted: storing at least three copies of data, on two different media, with one copy stored in an off-site disaster recovery center, to enhance data recovery capabilities and ensure that the power system can quickly return to normal operation after suffering an attack.

2.4 Security Monitoring and Intrusion Detection

Security monitoring and intrusion detection are key components of the power monitoring system’s defense system, identifying malicious attack behaviors by analyzing network traffic and system logs in real time, improving grid security.

First, at the network level, an intrusion detection system (IDS) based on Deep Packet Inspection (DPI) should be deployed, combined with traffic anomaly analysis models (such as K-Means clustering or LSTM recurrent neural networks), to detect attacks such as DDoS and data poisoning, controlling the false positive rate below 5%.

Second, at the host security monitoring level, an Endpoint Detection and Response (EDR) system based on behavior analysis should be adopted, using User and Entity Behavior Analytics (UEBA) to analyze user and device behavior patterns, detecting abnormal logins, privilege abuse, and malware implantation. 

Finally, for SCADA systems, industrial protocol anomaly detection technology can be introduced, using Finite State Machines (FSM) to analyze the legitimacy of commands from protocols such as Modbus and IEC 104, preventing protocol abuse attacks. In terms of log auditing and correlation analysis, a Security Information and Event Management (SIEM) system should be adopted to aggregate log data and perform real-time analysis through the ELK architecture, improving security visualization capabilities.

2.5 Emergency Response and Security Incident Management

Emergency response and security incident management for power monitoring systems need to cover threat identification, incident handling, traceability analysis, and recovery mechanisms to mitigate the impact of security incidents on power system operations. First, in the threat identification phase, based on a SOAR platform, alarm events should be automatically analyzed, and attack types evaluated by combining threat intelligence, improving the accuracy of event classification.

Second, in the incident handling phase, a tiered response mechanism should be adopted, classifying security incidents into levels I to IV, and corresponding measures taken according to the incident level, such as isolating infected terminals, blocking malicious IP addresses, or switching to a backup control center. For advanced persistent threats (APT), an active defense strategy based on threat hunting can be adopted, using YARA rules to detect hidden backdoors and improve attack detection rates. Finally, in the traceability analysis phase, through event retrospection and forensic analysis, combined with the Cyber Kill Chain attack graph, the attack path should be reconstructed, identifying the attacker’s tactics, techniques, and procedures (TTPs), providing a basis for subsequent security reinforcement.

3. Application of Key Information Security Technologies

3.1Blockchain-Based Power Data Traceability Solution

Blockchain technology, with its characteristics of decentralization, immutability, and traceability, provides a highly credible data traceability solution for power monitoring systems. In power data management, data integrity and credibility are key issues. Traditional centralized databases have risks of single-point failure and tampering. Blockchain uses distributed ledger technology to ensure the security of data storage.

First, at the data storage layer, hash chains are used to encrypt and store power monitoring data, with each piece of data generating a unique hash value linked to the previous block, ensuring data temporal consistency and immutability. Second, at the data sharing layer, a consortium chain architecture is used, setting grid dispatch centers, substations, and regulatory agencies as consortium nodes, verifying data authenticity through Byzantine Fault Tolerance consensus mechanisms, ensuring that data can only be modified by authorized nodes, enhancing data security.

Finally, in terms of data access control, a permission management mechanism based on smart contracts is combined, defining access rules to ensure that user access permissions are constrained by policies, avoiding unauthorized data calls. For example, by deploying smart contracts through the Hyperledger Fabric framework, operation and maintenance personnel are restricted to querying equipment operating status, while regulatory agencies can access complete historical data, ensuring data privacy and compliance.

3.2 Information Security Protection for Power Systems in 5G and Edge Computing Environments

The integrated application of 5G and edge computing in power monitoring systems enhances data processing efficiency and real-time response capabilities but also introduces new information security challenges. First, in terms of communication security, since 5G networks use network slicing architecture, independent security policies need to be configured for different service traffic to prevent cross-slice attacks.

End-to-end encryption (E2EE) technology should be adopted, combined with the Elliptic Curve Digital Signature Algorithm (ECDSA), to ensure that power dispatch data is not tampered with or stolen during transmission. Second, in terms of edge computing security, Trusted Execution Environment (TEE) should be deployed, such as Intel SGX or ARM TrustZone, to securely isolate edge nodes and prevent malicious code from intruding into critical control logic.

A decentralized identity authentication (DID) mechanism should be adopted, managing edge device access permissions through decentralized identifiers (Decentralized Identifier) to reduce credential leakage risks. Finally, for the issue of edge computing nodes being vulnerable to physical attacks, Hardware Root of Trust (RoT) technology should be adopted to perform remote integrity verification of device firmware, ensuring that devices have not been maliciously tampered with.

4. Conclusion

Information security technologies in power monitoring systems play an important role in ensuring stable grid operation and preventing cyberattacks. By constructing a multi-layered security protection system and adopting key technologies such as blockchain, 5G, edge computing, and encryption algorithms, data security, network defense capabilities, and access control accuracy can be effectively improved.

Combined with intelligent monitoring and emergency response mechanisms, real-time threat detection and rapid handling can be achieved, reducing security risks. With the development of grid digitization and intelligence, information security technologies will continue to evolve to address increasingly complex cyberattack methods, ensuring that power monitoring systems operate safely, stably, and efficiently over the long term.