A novel detection and defense mechanism against false data injection attack in smart grids

Cui, Jinlong; Gao, Beibei; Guo, Baojun

doi:10.1049/gtd2.12848

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In practice, the implementation of this approach entails the utilization of more intricate models and algorithms, potentially leading to heightened complexity. Reference [14] proposes an improved detection method based on principal component analysis, which introduced a mathematical transformation principal approach to improve the detection performance, such as the detection rate and false alarm rate. Nonetheless, specific prerequisites regarding data preprocessing and computational resources exist, rendering the method potentially inapplicable to all datasets.…”

Section: Fdia Detection Based On a Model-driven Approachmentioning

confidence: 99%

The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode

Zhou,

Li,

Zang

et al. 2023

Applied Sciences

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Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system’s network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN’s generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks.

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Section: Fdia Detection Based On a Model-driven Approachmentioning

confidence: 99%

The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode

Zhou,

Li,

Zang

et al. 2023

Applied Sciences

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“…Active attack selectively destroys the effectiveness and integrity of information by obtaining target information, finding out system vulnerabilities and invading the system. Passive attack can intercept, steal and decipher important confidential information [7][8] without affecting the normal operation of the network. With the rapid development of information technology, network attacks have become a global problem, posing a serious threat to individuals, enterprises and even national security.…”

Section: Introductionmentioning

confidence: 99%

Defense method of quantum secure communication network attacks based on AES algorithm and time window

Huang,

Lao,

Lin

et al. 2024

International Conference on Signal Processing and Communication Security (ICSPCS 2024)

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Due to the different action modes of communication network attacks, it is difficult to guarantee the effect of defense technology in information loss control. Therefore, a quantum secure communication network attack defense method based on AES algorithm and time window is proposed. AES algorithm is introduced to group the original quantum secure communication data plaintext to obtain the corresponding data blocks. After multi-level conversion processing, the intermediate results generated by each conversion are taken as the state parameters, and data encryption is completed through byte substitution operation, row transformation operation, column mixing operation and round key transformation operation; When defending against quantum secure communication network attacks, the network state decision time window is established. From the perspective of the traffic status in the window, the attack status and location are analyzed and judged, and the dynamic adjustment of the traffic limit strategy is adopted for attack defense. In the test results, the communication packet loss rate is the lowest, basically stable within 3.0%, which has obvious advantages compared with the control group.

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Detection‐based resilient control for cyber‐physical systems against two‐channel false data injection attacks

Li,

Ren

et al. 2024

Intl J Robust & Nonlinear

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This paper focuses on a detection‐based resilient control issue for cyber‐physical systems (CPSs) subject to false data injection (FDI) attacks, where FDI attacks occur in the communication channels from the sensor‐to‐controller and controller‐to‐actuator. Firstly, to reduce the adverse impacts of FDI attacks on estimation performance, an unbiased estimator is constructed by tackling the equality‐constrained optimization problem. Then, an effective attack detection mechanism is devised by introducing a pseudo‐innovation sequence to formulate the detection function, which can successfully detect two‐channel FDI attacks. Based on these detection results, a resilient controller combining linear quadratic Gaussian and controllers is provided to guarantee the mean‐square asymptotic stability of CPSs with performance. Finally, the validity of the proposed resilient control approach is demonstrated by a simulation involving satellite control system.

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A novel detection and defense mechanism against false data injection attack in smart grids

Cited by 6 publications

References 33 publications

The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode

The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode

Defense method of quantum secure communication network attacks based on AES algorithm and time window

Detection‐based resilient control for cyber‐physical systems against two‐channel false data injection attacks

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