Coordinated data falsification attack detection in the domain of distributed generation using deep learning

Bhusal, Narayan; Gautam, Mukesh; Shukla, Raj Mani; Benidris, Mohammed; Sengupta, Shamik

doi:10.1016/j.ijepes.2021.107345

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…Among a variety of attacks that act similarly to data injection attack (FDI), we highlight camouflage, recommendation, and deduction, each one with its peculiarities 29 . For simplicity, we adopt FDI as the standard because it is more insistent against data‐oriented networks like IIoT.…”

Section: A System For Mitigating Fdi Attacks In Iiot Networkmentioning

confidence: 99%

“…Among a variety of attacks that act similarly to data injection attack (FDI), we highlight camouflage, recommendation, and deduction, each one with its peculiarities. 29 For simplicity, we adopt FDI as the standard because it is more insistent against data-oriented networks like IIoT. We apply a FDI attack model based on Deng et al 25 ; we adapt the way the attack acts on the data to change only one data at a time, instead of the data array as in the original.…”

Section: Fdi Attack Modelmentioning

confidence: 99%

“…Hence, those behaviors make the attack difficult to identify and increase network malfunction times. Besides, a variety of other attacks, like camouflage, recommendation, and deduction, act in a similar way to the FDI attack, each one of them with its peculiarities 29 . Therefore, the fast identification and isolation of malicious devices in IIoT reduce long periods of network malfunctions that can affect the data dissemination service, by mitigating that those misbehaving devices generate inconsistent sensed data.…”

Section: Introductionmentioning

confidence: 99%

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Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

Pedroso

Santos

2022

Int J Network Mgmt

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The Internet of Things (IoT) has made possible the development of increasingly driven services, like industrial Industrial Internet of Things (IIoT) services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection (FDI) attacks stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONsensus Based Data FIlteriNg for IIoT (CONFINIT), an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared with Dynamic Data-aware Firefly-based Clustering (DDFC) increased by up to 35%-40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates (DRs) of 99%, accuracy of 90, and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CON-FINIT achieved DRs of 100%, accuracy of 99, and F1 of 0.93 with less than 2% of false positives and negatives rates.

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Section: A System For Mitigating Fdi Attacks In Iiot Networkmentioning

confidence: 99%

Section: Fdi Attack Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

Pedroso

Santos

2022

Int J Network Mgmt

View full text Add to dashboard Cite

show abstract

“…Among a variety of attacks that act similarly to data injection attack (FDI), we highlight camouflage, recommendation, and deduction, each one with its peculiarities 29 . For simplicity, we adopting FDI as the standard because it is more insistent against data-oriented networks like IIoT.…”

Section: False Data Injection Attack Modelmentioning

confidence: 99%

“…Hence, those behaviors make the attack difficult to identify and increases network malfunction times. Besides, a variety of other attacks, like camouflage, recommendation, and deduction, act in a similar way to the FDI attack, each one of them with its peculiarities 29 . Therefore, the fast identification and isolation of malicious devices in IIoT reduce long periods of network malfunctions, that can affect the data dissemination service, by mitigating that those misbehaving devices generate inconsistent sensed data.…”

Section: Introductionmentioning

confidence: 99%

Dissemination Control in Dynamic Data Clustering For Dense IIoT Against False Data Injection Attack

Pedroso¹,

Santos²

2022

Preprint

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The IoT has made possible the development of increasingly driven services, like industrial IIoT services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection attacks (FDI) stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONFINIT, an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared to DDFC increased by up to 35% -40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates of 99%, accuracy of 90 and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CONFINIT achieved detection rates of 100%, accuracy of 99 and F1 of 0.93 with less than 2% of false positives and negatives rates.

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Privacy-preserving distributed state estimation in smart grid

Dai,

Yang,

et al. 2024

Electric Power Systems Research

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Coordinated data falsification attack detection in the domain of distributed generation using deep learning

Cited by 17 publications

References 28 publications

Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

Dissemination control in dynamic data clustering for dense IIoT against false data injection attack

Dissemination Control in Dynamic Data Clustering For Dense IIoT Against False Data Injection Attack

Privacy-preserving distributed state estimation in smart grid

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