AI and Security of Critical Infrastructure

Sakhnini, Jacob; Karimipour, Hadis; Parizi, Reza M.

doi:10.1007/978-3-030-38557-6_2

Cited by 12 publications

(5 citation statements)

References 49 publications

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“…Azmoodeh et al in (3) highlighted that the most crucial parameters in order to use a Deep Learning model is the generalization of the model and the selection of the correct training parameters (e.g batch size, learning rate, weight decay).…”

Section: Model Selectionmentioning

confidence: 99%

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos

Sarigiannidis

Ioannidis

et al. 2021

Computer Science Review

193

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Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

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Section: Model Selectionmentioning

confidence: 99%

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos

Sarigiannidis

Ioannidis

et al. 2021

Computer Science Review

193

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show abstract

“…Such methods are Cyber Kill Chain [284] and Diamond Model of Intrusion Analysis [285] (or a combination of the two [279]). Moreover, modern approaches for providing security in this sector are considered, like game-theory-based [286], blockchain-based [287], or AIbased methods [288].…”

Section: ) Attacks and Countermeasuresmentioning

confidence: 99%

Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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5G is the fifth-generation cellular network satisfying the requirements IMT-2020 (International Mobile Telecommunications-2020) of the International Telecommunication Union. Mobile network operators started using it worldwide in 2019. Generally, 5G achieves exceptionally high values of performance parameters of access and transmission. The application of edge servers facilitated the implementation of such requirements of 5G, which resulted in 5G MEC (Multi-access Edge Computing) technology. Moreover, to optimize services for specific business applications, the concept of 5G vertical industries has been proposed. In this paper, we study how the application of the MEC technology affects the functioning of 5G MEC-based services. We consider twelve representative vertical industries of 5G MEC by presenting their essential characteristics, threats, vulnerabilities, and known attacks. Next, we analyze their functional properties, give efficiency patterns and identify the effect of applying the MEC technology in 5G on the resultant network's quality parameters to identify the expected security requirements. Finally, we identify the impact of classified threats on the 5G empowered vertical industries and identify the most sensitive cases to focus on their protection against network attacks in the first place.

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“…AI security is becoming an increasingly critical topic [9], especially with the growing number of connected devices and systems. With the proliferation of the Internet of Things (IoT) and the digitization of various industries [10], the need to preserve the security and privacy of data becomes inevitable.…”

Section: Introductionmentioning

confidence: 99%

Research Trends in Artificial Intelligence and Security—Bibliometric Analysis

Ilić,

Šijan,

Predić

et al. 2024

Electronics

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This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.

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AI and Security of Critical Infrastructure

Cited by 12 publications

References 49 publications

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Verticals in 5G MEC-Use Cases and Security Challenges

Research Trends in Artificial Intelligence and Security—Bibliometric Analysis

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