A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection

Haghnegahdar, Lida; Wang, Yong

doi:10.1007/s00521-019-04453-w

Cited by 101 publications

(42 citation statements)

References 35 publications

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“…Haghnegahdar and Wang (2020) proposed a novel intrusion detection model in cyber security which classifies the binary-class, triple-class, and multi-class cyber-attacks and power-system incidents.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Praveena

Bharathi

2021

Concurrent Engineering

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Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Praveena

Bharathi

2021

Concurrent Engineering

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“…Zhou et al [146] built a stacked denoising autoencoder (SDAE) neural network model to identify and classify four attacks in the smart grid with an accuracy as high as 96%. Cui et al [147] used an intrusion detection model for smart grid intrusion detection, which is based on a whale optimization-trained ANN algorithm with one hidden layer. Kosek [148] also used a ANN-based model to discover malicious voltage control actions in the low-voltage distribution grid.…”

Section: Smart Grid Securitymentioning

confidence: 99%

“…Table 4 summarizes the AI techniques for smart grid security. [148] 2016 Detect malicious voltage control actions ANN Ozay et al [154] 2016 Attack detection KNN, SVM Tan et al [143] 2016 Survey Data-driven approach Zhou et al [146] 2018 Attacks detection SDAE Ahmed et al [152] 2018 Detect covert cyber deception assault SVM Zhang et al [22] 2018 Survey DL, RL Ni et al [150] 2019 Attacks detection RL Hossain et al [144] 2019 Survey Big data, ML Ahmed et al [153] 2019 Detect covert cyber deception assault Isolation forest Li et al [155] 2019 Electricity theft detection CNN, random forests Cui et al [145] 2020 Survey ML Ali et al [4] 2020 Survey AI Haghnegahdar et al [147] 2020 Attacks detection ANN Zhang et al [151] 2020 Intrusion detection Domain-Adversarial Learning…”

Section: Smart Grid Securitymentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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“…Haghnegahdar and Wang [10] proposed an intrusion detection model based on an artificial neural network, which is trained with the Whale Optimization Algorithm (WOA). WOA is applied to initialize and adjust the weight vector of the ANN to achieve the minimum mean square error.…”

Section: Introductionmentioning

confidence: 99%

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

2021

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The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.

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A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection

Cited by 101 publications

References 35 publications

RETRACTED: An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

RETRACTED: An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Artificial Intelligence Techniques in Smart Grid: A Survey

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

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