A Comprehensive Survey on Cyber-Physical Smart Grid Testbed Architectures: Requirements and Challenges

Smadi, Abdallah A.; Ajao, Babatunde; Johnson, Brian K.; Lei, Hangtian; Chakhchoukh, Yacine; Al‐Haija, Qasem Abu

doi:10.3390/electronics10091043

Cited by 67 publications

(33 citation statements)

References 142 publications

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“…There is a number of IoT technology-hacking scenarios as illustrated in [41,42] that could cause a high level of harm to the system. IoT information security issues are associated with the preservation of authentication, authorization, integrity, confidentiality, non-repudiation, availability, and privacy [43,44]. Security issues and challenges related to IoT technologies can be approached from aspects of issues associated with different IoT layers.…”

Section: Iot Security Challengesmentioning

confidence: 99%

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Albulayhi

Smadi

Sheldon

et al. 2021

Sensors

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This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

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Section: Iot Security Challengesmentioning

confidence: 99%

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Albulayhi

Smadi

Sheldon

et al. 2021

Sensors

Self Cite

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“…The model of the Steiner tree problem may be used for the distribution grids optimization (reengineering) [12]. It is a generalisation of the classical Steiner problem, similar methods, particularly evolutionary algorithms are used to solve it.…”

Section: State-of the Art And Objectivesmentioning

confidence: 99%

Topological structure reengineering regional electric power systems

Nechyporenko

Hubarenko

2022

reks

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This paper analyses the topology of a regional power system distribution grid. This research improves the efficiency of the electric power system's operation by using upgrading the redesign (reengineering) methods of topological structures within distribution grids. The research object is an electric power system that consists of generation, transmission and distribution parts and requires reengineering. The subject of research is the re-engineering of the topology of the power system distribution grid. To achieve the research purpose, modifications of k-means algorithm as well as the small step algorithm based on the statistical analysis, clustering and minimum spanning tree development methods of Prim and Kraskal are used. The modifications described in this paper allow for optimization of the network based on user needs, properties of the operating grid elements, and other additional constraints for flexibility and generality. Given the varying parameters, this method provides the means to redesign parts of the distribution grid, keeping its certain elements safe from displacement, but also the means to redesign the whole distribution grid, including changes to the number and location of transformer substations and transmission lines. Conclusions. To solve the problem of determining the territorially close groups of consumers in the paper, it was proposed to use the k-means clustering algorithm. This algorithm allows us to divide consumer sets into clusters, so that coordinates of their centres will be recommended as locations of transformer substations. The modernization of the k-means algorithm was proposed by developing procedures for adding and combining clusters using different strategies for determining starting centroids. Based on this, a method for reengineering the topological structures of regional electric power systems in terms of the possibility of their fundamental restructuring was developed. The results of this research may be useful to various enterprises, organizations or institutes dealing with the elaboration or design of electric power system development on the corporate, regional or local level.

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“…IoT technology has touched almost every pitch of everyday life with its widespread applications. This, in turn, has substantially improved our life quality as a result of adopting the IoT "know-how" of several life, which have the potential to collect, harvest, and investigate data concerning the adjoining environment [1]. This context has accelerated the improvement of smart cities by enabling communication between things (machines) and between machines and humans.…”

Section: Introductionmentioning

confidence: 99%

Anomaly-based Intrusion Detection System for IoT Networks With Improved Data Engineering

Alsulami¹,

Al‐Haija²,

Tayeb³

2022

Preprint

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Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people's daily lives. However, the IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. Therefore, this research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories, normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes models shallow neural networks (SNN), decision trees (DT), bagging trees (BT), support vector machine (SVM), and k-nearest neighbor (kNN). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was applied to the dataset to improve the accuracy of the learning models. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4%-99.9% recorded for the classification process.

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A Comprehensive Survey on Cyber-Physical Smart Grid Testbed Architectures: Requirements and Challenges

Cited by 67 publications

References 142 publications

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Topological structure reengineering regional electric power systems

Anomaly-based Intrusion Detection System for IoT Networks With Improved Data Engineering

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