A Privacy-Preserving-Framework-Based Blockchain and Deep Learning for Protecting Smart Power Networks

Keshk, Marwa; Turnbull, Benjamin; Moustafa, Nour; Vatsalan, Dinusha; Choo, Kim‐Kwang Raymond

doi:10.1109/tii.2019.2957140

Cited by 122 publications

(48 citation statements)

References 22 publications

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“…Though sufficiently accurate against known attacks for which signatures exist in the repository, this technique cannot detect zero day (new) attacks. Even if it is not effective against mutations of an existing attack [54,96,97].…”

Section: Signature-based Detection Techniquesmentioning

confidence: 99%

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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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Section: Signature-based Detection Techniquesmentioning

confidence: 99%

“…Anomaly-based detection techniques rely on a baseline normal behavior profile for the monitored environment [97,102]. This normal baseline is then used for comparison of system actions at any given moment.…”

Section: Anomaly-based Detection Techniquesmentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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“…Gai et al [58] aimed to protect the privacy of the users in a smart grid by incorporating a permissioned blockchain with smart contracts and edge computing in which group signatures and covert channel authorization techniques are utilized. Keshk et al [59] devised a framework to provide data privacy and security in smart power networks with two modules; The first being a two-level module that is dedicated to verify data integrity using proof of work blockchain along with applying a variational autoencoder to transform data, and the second being an anomaly detection module for training and validating the output of the first module. This second module uses deep learning techniques.…”

Section: ) Security Enhancement Techniquesmentioning

confidence: 99%

A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture

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Turnbull

et al. 2021

IEEE Open J. Comput. Soc.

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Blockchain and smart contracts have seen significant application over the last decade, revolutionising many industries, including cryptocurrency, finance and banking, and supply chain management. In many cases, however, the transparency provided potentially comes at the cost of privacy. Blockchain does have potential uses to increase privacy-preservation. This paper outlines the current state of privacy preservation utilising Blockchain and Smart Contracts, as applied to a number of fields and problem domains. It provides a background of blockchain, outlines the challenges in blockchain as they relate to privacy, and then classifies into areas in which this paradigm can be applied to increase or protect privacy. These areas are cryptocurrency, data management and storage, e-voting, the Internet of Things, and smart agriculture. This work then proposes PPSAF, a new privacy-preserving framework designed explicitly for the issues that are present in smart agriculture. Finally, this work outlines future directions of research in areas combining future technologies, privacy-preservation and blockchain.

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“…Smart grids, blockchain, consensus algorithm, industries, renewable energy sources 2019 [125] A survey on the implementation of differential privacy in the healthcare and medical systems, the energy systems, transportation systems, and industrial IoT.…”

Section: [93]mentioning

confidence: 99%

“…The attacker cost comprises the knowledge and the resources mandatory for mitigating the attack. Meanwhile, the defender costs include power outages, equipment damages, and economic losses [125].…”

Section: Financial Threatsmentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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A Privacy-Preserving-Framework-Based Blockchain and Deep Learning for Protecting Smart Power Networks

Cited by 122 publications

References 22 publications

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

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