A Review of Detection, Evolution, and Data Reconstruction Strategies for False Data Injection Attacks in Power Cyber-Physical Systems

• URL: http://arxiv.org/abs/2501.10441v1
• Date: Mon, 13 Jan 2025 22:28:04 GMT
• Title: A Review of Detection, Evolution, and Data Reconstruction Strategies for False Data Injection Attacks in Power Cyber-Physical Systems
• Authors: Xiaoyong Bo,
• Abstract summary: Integration of information and physical systems in modern power grids has heightened vulnerabilities to False Data Injection Attacks (FDIAs) This paper reviews FDIA detection, evolution, and data reconstruction strategies, highlighting cross-domain coordination, multi-temporal evolution, and stealth characteristics.
• Score: 0.0
• License:
• Abstract: The integration of information and physical systems in modern power grids has heightened vulnerabilities to False Data Injection Attacks (FDIAs), threatening the secure operation of power cyber-physical systems (CPS). This paper reviews FDIA detection, evolution, and data reconstruction strategies, highlighting cross-domain coordination, multi-temporal evolution, and stealth characteristics. Challenges in existing detection methods, including poor interpretability and data imbalance, are discussed, alongside advanced state-aware and action-control data reconstruction techniques. Key issues, such as modeling FDIA evolution and distinguishing malicious data from regular faults, are identified. Future directions to enhance system resilience and detection accuracy are proposed, contributing to the secure operation of power CPS.

Related papers

• Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids [2.0766068042442174]
  We introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems.
  arXiv  Detail & Related papers  (2024-12-09T07:07:17Z)
• An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids [0.0]
  This paper proposes an unsupervised adversarial autoencoder (AAE) model to detect false data injection attacks (FDIAs) in unbalanced power distribution grids. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements. It is tested on IEEE 13-bus and 123-bus systems with historical meteorological data and historical real-world load data.
  arXiv  Detail & Related papers  (2024-03-31T01:20:01Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Investigation of Multi-stage Attack and Defense Simulation for Data Synthesis [2.479074862022315]
  This study proposes a model for generating synthetic data of multi-stage cyber attacks in the power grid. It uses attack trees to model the attacker's sequence of steps and a game-theoretic approach to incorporate the defender's actions.
  arXiv  Detail & Related papers  (2023-12-21T09:54:18Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security [3.8237485961848128]
  Cross-Layer Ensemble CorrDet with Adaptive Statistics is presented. It integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays. Results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score.
  arXiv  Detail & Related papers  (2021-11-10T00:00:51Z)
• Multi-Source Data Fusion for Cyberattack Detection in Power Systems [1.8914160585516038]
  We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
  arXiv  Detail & Related papers  (2021-01-18T06:34:45Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.