Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems

Chen, Haoying; Zhang, Yiyi; Zhao, Junhui; Manla, Emad

doi:10.3390/en12061084

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…The computing capability of the client

k

{Comp}^{k}

(Huang et al, 2020; Nishio & Yonetani, 2019). The client's risk of attack (denoted as

R_{k}

) (Chen et al, 2019) are used as the multiplication factor for the client's selection. The weights of client

k

in round

t

are as follows:

q_{t}^{k} goodbreak= {Comm}_{t}^{k} * {Comp}_{t}^{k} * R_{k}

\frac{1}{T} \sum_{t = 1}^{T} a_{t, k} \geq β_{value}

Formula

((), 9)

is the introduced long‐term fairness constraint, where

a_{t, k}

indicates whether client

k

is selected for model training in

t

‐rounds of federated learning.…”

Section: Client Selection‐based Fed_adbn Frameworkmentioning

confidence: 99%

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Fed_ADBN: An efficient intrusion detection framework based on client selection in AMI network

et al. 2022

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Data transmission between smart meters and data center is facing network security threats in advanced metering infrastructure of smart grid. The traditional solution is to move the data to the data center to build a centralized attack detection model, or divide the collected data into several independent and identically distributed datasets to build a distributed attack detection model. However, the long-distance transmission and the centralized storage of data not only increase the communication overhead and time overhead, but also increase the risk of being attacked, causing privacy disclosure during the process of building the model. In this paper, we propose an efficient intrusion detection framework Fed_ADBN based on federated attention deep belief network and client selection. Clients cooperate with the data center to jointly build a horizontal federated learning framework. Under the premise of protecting data security by keeping data on the clients, we design a client selection algorithm based on client computing power, communication quality and security risks, which can improve the operating efficiency of federated learning. We also deploy a deep belief neural network with attention mechanism in each client to accurately detect possible network attacks in AMI network in real time. Experimental results show that compared with state-of-the-art methods, the proposed framework can not only maintain good detection accuracy but also protect privacy.

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“…The computing capability of the client

k

{Comp}^{k}

(Huang et al, 2020; Nishio & Yonetani, 2019). The client's risk of attack (denoted as

R_{k}

) (Chen et al, 2019) are used as the multiplication factor for the client's selection. The weights of client

k

in round

t

are as follows:

q_{t}^{k} goodbreak= {Comm}_{t}^{k} * {Comp}_{t}^{k} * R_{k}

\frac{1}{T} \sum_{t = 1}^{T} a_{t, k} \geq β_{value}

Formula

((), 9)

is the introduced long‐term fairness constraint, where

a_{t, k}

indicates whether client

k

is selected for model training in

t

‐rounds of federated learning.…”

Section: Client Selection‐based Fed_adbn Frameworkmentioning

confidence: 99%

“…The computing capability of the client k is Comp k(Huang et al, 2020;Nishio & Yonetani, 2019). The client's risk of attack (denoted as R k )(Chen et al, 2019) are used as the multiplication factor…”

mentioning

confidence: 99%