A Review of Power System False Data Attack Detection Technology Based on Big Data
Abstract
:1. Introduction
- (1)
- Keyword selection: Choose keywords that accurately reflect the topic and research content of the article. Make sure that the selected keywords cover all aspects of power system big data analysis and false data attack detection. For example: “big data”, “power system”, “false data attack”, “blockchain”, “state estimation”, “machine learning” and “data-driven”, etc.
- (2)
- Publication date of the literature: Literature published within the past 3–5 years is usually selected to ensure that the technologies and methods discussed are the latest and most relevant. Big data technology and power system security are developing rapidly, and early literature may be outdated and cannot reflect the current research progress and technical level.
- (3)
- Research quality: Consider articles published in journals or conferences with high impact factors, as well as articles that are widely cited. A higher impact factor for a journal generally means higher quality articles. Articles with many citations often have a greater impact on other research in the field. The specific evaluation method follows: Perform a preliminary screening based on the title and abstract and eliminate irrelevant articles. Find the impact factor of the journal or conference where the article was published. Use Google Scholar or other academic search engines to check the number of citations of the article.
- (4)
- Relevance of research methods: Select the literature that uses advanced big data analysis methods or power system-specific false data detection techniques in the research. Read the abstract and method section of the literature to confirm whether the methods used are relevant to big data and false data attack detection. Compare the methods of different studies and select the most innovative and practical research.
- (5)
- Importance of findings: Select studies that have a significant impact on practical applications or significantly advance the theory. Investigate whether the research results can be applied to the safety protection of actual power systems and whether the research proposes new theories and models and makes important contributions to the academic community. Read the conclusion part of the literature and analyze the practical application and theoretical contribution of its research results.
2. Application Status of Big Data Technology in the Power Industry
2.1. Optimization of Energy Production
2.2. Analysis of Energy Consumption
2.3. User Service Improvements
3. Overview of False Data Attacks
3.1. False Data Injection Attack
3.2. Timestamp Tampering Attack
3.3. Data Deletion and Tampering Attacks
3.4. Denial of Service Attack
4. Analysis of False Data Attacks in Four Aspects of Power Big Data System
4.1. Data Collection Stage
4.1.1. State Estimation
4.1.2. Machine Learning
4.1.3. Data Driven
4.2. Data Transmission Stage
Time Synchronization Monitoring
4.3. Data Processing and Analysis Stage
- (1)
- Strengthen access control to the power system and limit the deletion and modification permissions of key data.
- (2)
- Implement data backup and disaster recovery mechanisms to ensure data reliability and durability.
- (3)
- Use secure encryption technology to protect data transmission and storage to prevent attackers from tampering with or deleting data.
- (4)
- Digitally sign or hash check key data to verify data integrity and authenticity.
- (5)
- Establish data audit and monitoring mechanisms to detect abnormal and malicious behavior promptly.
4.3.1. Data Integrity Verification
4.3.2. Blockchain Technology
4.4. Control and Response Stage
4.4.1. Traffic Supervision
4.4.2. Statistics
4.4.3. Elastic Computing
5. Research Challenges and Future Directions
5.1. Covert New Adversarial Attack Detection
5.2. How to Further Unleash the Potential of Big Data in the Power Sector
5.3. Power Big Data Security and Privacy Protection
6. Conclusions
- (1)
- The accuracy and security of data acquisition equipment and how they resist external interference and tampering.
- (2)
- Encryption and authentication technology during data transmission to ensure the integrity and confidentiality of data as it passes through different network nodes and media.
- (3)
- Algorithm robustness in the data processing and analysis phase to identify and filter out false or abnormal data.
- (4)
- The design of the control and response system so that quick action can be taken when an attack is detected.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author | Method | Advantages | Disadvantages |
---|---|---|---|
Chen et al. [41] | Multi-level static estimation | Fast state estimation | Difficulty in selecting different interface quantities between subsystems |
Qu et al. [43] | Trust worthiness WLS algorithm | Improving the robustness of state estimation | Additional computational burden and potential false positives |
Khalid et al. [44] | Median Regression Function | Comprehensively consider various factors that affect state estimation | Difficult to respond quickly |
Tang et al. [45] | Generalized Likelihood Ratio Test (GLRT) Detector | Can flexibly adapt to different data distributions and model assumptions | Inaccurate estimates for small samples |
Author | Method | Advantages | Disadvantages |
---|---|---|---|
Dayananda et al. [50] | Reconfigurable Kalman Filter | Flexible and adaptable to dynamic environments | Sensitivity of threshold selection |
Wang et al. [51] | KFRNN | Able to handle complex attack scenarios | New attack detection not applicable |
Luo et al. [52] | Adaptive Kalman Filter Library | Reduced latency and no prior knowledge required | Adaptive threshold parameter adjustment is difficult |
Rashed et al. [53] | Cluster partition state estimation technology | Dynamic and static combined state estimation method | Lack of anti-attack ability |
Živković et al. [54] | UKF-WLS | Quickly identify fake data attacks | Differences between noise impact estimates |
Lu et al. [55] | Improved Unscented Kalman Filter | Improve state estimation accuracy and robustness | Limited forecast accuracy |
Author | Method | Advantages | Disadvantages |
---|---|---|---|
Moradi et al. [79] | Clock comparison PTP attack detection | Detects multiple types of PTP attacks | Affected by third-party time sources and GPS signals |
Moussa et al. [80] | Extending PTP to detect time synchronization attacks | Enhance the security of the PTP protocol and reduce the attack surface | The UPPAAL model checker has limited portability and applicability |
Alghamdi et al. [81] | Trusted Supervisor Node (TSN) approach | TSN can detect abnormal patterns that point to attacks, improving detection accuracy | Inability to detect external or advanced attacks |
Qiu et al. [82] | Secure Time Synchronization Protocol | Reduce the impact of malicious nodes on the network | Protocol compatibility issues need to be considered |
Wu et al. [83] | Distributed timestamp mechanism | Continuously verifiable to enhance the credibility of timestamps | High deployment complexity |
Moussa et al. [84] | Simple Network Management Protocol (SNMP) | Easy to use and cost effective | Difficult to apply to large-scale networks |
He et al. [85] | Polarization Coded Synchronization Security Strategy | Polar encoding can encrypt timestamps | High network overhead |
Hymlin et al. [86] | Clustering-based timestamp mechanism | Dynamic clustering adapts to network changes | Unable to respond to new types of attacks |
Author | Method | Advantages | Disadvantages |
---|---|---|---|
Wang et al. [87] | MAC | Reduced additional delay | Difficulty in key distribution |
Ateniese et al. [88] | RSA | Simple key management | The key length requirement is high |
Shen et al. [89] | BLS | Scalability | Undo is not supported |
Erway et al. [90] | Dynamic Operation | Simplify data management | Data availability risks |
Liu et al. [91] | Multiple copies | Has a certain ability to resist attacks | Synchronization delay |
Jayaraman et al. [92] | privacy protection | Emphasis on anonymity | Difficulty balancing privacy protection and data utility |
Wang et al. [94] | TPA dynamic data integrity verification | Improve the efficiency of the verification process | There is a risk of third-party auditors |
Ge et al. [95] | Dynamic search of symmetric keys | Able to meet the needs of frequent data changes | Increase system resource consumption |
Liu et al. [96] | VDERS | Meets the need for data sorting and searching | Not applicable when dealing with large-scale datasets |
Fu et al. [97] | DIPOR | Improved data availability and integrity | Not applicable when dealing with large-scale datasets |
Author | Method | Advantages | Disadvantages |
---|---|---|---|
Mousavi et al. [111] | Fixed Threshold | Easy to use | General limitations |
Aladaileh et al. [112] | Dynamic threshold based on the combined entropy of benevolence and righteousness | Dynamic adaptability | Unable to respond to new types of attacks |
David et al. [113] | Traffic characteristics + dynamic threshold | Lower false positive rate | Threshold adjustment strategies require careful consideration |
Tsobdjou et al. [114] | Dynamic Threshold of Online Entropy | Reduce false positives and false negatives | Entropy instability |
Baskar et al. [115] | Multi-threshold traffic analysis | High detection rate | Unable to respond to new types of attacks |
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Chang, Z.; Wu, J.; Liang, H.; Wang, Y.; Wang, Y.; Xiong, X. A Review of Power System False Data Attack Detection Technology Based on Big Data. Information 2024, 15, 439. https://doi.org/10.3390/info15080439
Chang Z, Wu J, Liang H, Wang Y, Wang Y, Xiong X. A Review of Power System False Data Attack Detection Technology Based on Big Data. Information. 2024; 15(8):439. https://doi.org/10.3390/info15080439
Chicago/Turabian StyleChang, Zhengwei, Jie Wu, Huihui Liang, Yong Wang, Yanfeng Wang, and Xingzhong Xiong. 2024. "A Review of Power System False Data Attack Detection Technology Based on Big Data" Information 15, no. 8: 439. https://doi.org/10.3390/info15080439
APA StyleChang, Z., Wu, J., Liang, H., Wang, Y., Wang, Y., & Xiong, X. (2024). A Review of Power System False Data Attack Detection Technology Based on Big Data. Information, 15(8), 439. https://doi.org/10.3390/info15080439