Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

Khoei, Tala Talaei; Slimane, Hadjar Ould; Kaabouch, Naima

doi:10.4236/cn.2022.144009

Cited by 27 publications

(9 citation statements)

References 177 publications

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“…In the existing literature, several surveys have been carried out to discuss different aspects of cyberattacks on smart grids. The authors in [28], [29] summarized cyberattacks, detection, and countermeasure techniques in smart grids. A summary of detection techniques for FDIAs using machine learning is provided in [30].…”

Section: ) Authenticitymentioning

confidence: 99%

Command Injection Attacks in Smart Grids: A Survey

Usama,

Aman

2024

IEEE Open J. Ind. Applicat.

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Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The False Command Injection Attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This paper presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of FCIAs on smart grids are also discussed. Subsequently, this paper provides an extensive review of detection studies, categorizing them based on the type of detection technique employed.

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Section: ) Authenticitymentioning

confidence: 99%

Command Injection Attacks in Smart Grids: A Survey

Usama,

Aman

2024

IEEE Open J. Ind. Applicat.

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“…Deep learning networks have complex structures and involve a large number of hyperparameters, so most of them are plagued by extremely time-consuming training processes [29]. To solve this problem, the broad learning system [11] is proposed.…”

Section: Broad Learning Systems In Medical Imagesmentioning

confidence: 99%

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

Zhang,

Qian,

et al. 2024

IEEE Access

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Retinal disease detection and diagnosis relying solely on artificial retinal diseases will bring great pressure to doctors and increase the rate of misdiagnosis. Therefore, the development of computer vision technology has brought the possibility for ophthalmologists to use computer-aided diagnosis. In recent years, most models for retinal disease recognition have been based on deep learning, which has the disadvantage of requiring large amounts of data and training time. It is also partly based on broad learning and its disadvantages are that feature extraction ability is limited and poor scalability. To overcome these limitations, we propose a novel artificial intelligence-based approach for automatic assessment of retinal disease images called multi-view deep-broad learning network (MDBL-Net), which absorbs the advantages of deep learning and broad learning. MDBL-Net comprises a Multi-view and Multi-scale Feature Extraction (MMFE) module and a Multi-scale Aggregation (MA) block. The MMFE module extracts features of different scales by learning feature representations from multiple views, while the MA block fully aggregates multi-scale deep-broad features from low-level to high-level representations. Experiments were conducted on two public datasets, UCSD and OCT2017. Experiments were conducted on two public datasets, UCSD and OCT2017, and results demonstrate that MDBL-Net achieves high accuracy even with limited training data (only 1%) and significantly reduces training time compared to traditional deep learning models. Specifically, MDBL-Net achieved an accuracy of 96.93% on the UCSD dataset and 99.90% on the OCT2017 dataset, outperforming state-of-the-art models in both cases. These findings suggest that the proposed MDBL-Net approach holds great promise for the task of retinal disease screening and recognition." INDEX TERMS Broad learning, image classification, medical image, retinal disease, computer aided diagnosis.

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“…Further different types of attacks are graphically classified in Fig. 2 (Khoei et al, 2022). Technical solutions such as advanced email filtering, endpoint protection, and continuous security audits are also instrumental in mitigating the risks associated with social engineering.…”

Section: Types Of Social Engineering Attacksmentioning

confidence: 99%

A comprehensive survey on social engineering-based attacks on social networks

Naz,

Sarwar,

Kaleem

et al. 2024

Int. j. adv. appl. sci.

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Threats based on social engineering in social networks are becoming a more common problem. Social engineering is a type of attack that relies on trickery and exploiting human psychology to gain access to confidential information or resources. It involves deceptive techniques like phishing, pretexting, and baiting, tricking individuals into revealing sensitive information or performing specific actions. These tactics can lead to unauthorized access to user accounts, identity theft, or the distribution of harmful content. This study offers a detailed review of threats related to social engineering on social networks. It explores various social engineering attacks, the methods used to execute these threats, and measures that can be adopted to minimize the risk of becoming a victim. The research aimed to develop a new, broad classification of social engineering attacks and strategies for responding to them. It also examines the challenges that social engineering poses to algorithms on social media platforms and highlights the need for more research. The study concludes by pointing out the shortcomings of current approaches and suggesting future research directions, stressing the importance of standardized protective measures and increasing awareness among potential victims. This thorough examination improves our understanding of social engineering attacks and encourages the development of innovative solutions and ethical practices, contributing to a more secure digital environment.

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Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

Cited by 27 publications

References 177 publications

Command Injection Attacks in Smart Grids: A Survey

Command Injection Attacks in Smart Grids: A Survey

A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

A comprehensive survey on social engineering-based attacks on social networks

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