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Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0 — A Survey

Azambuja, Antonio João Gonçalves de ; Plesker, Christian ; Schützer, Klaus ; Anderl, Reiner ; Schleich, Benjamin ; Almeida, Vilson Rosa (2023)
Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0 — A Survey.
In: Electronics, 2023, 12 (8)
doi: 10.26083/tuprints-00023791
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0 — A Survey
Language: English
Date: 12 May 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Publisher: MDPI
Journal or Publication Title: Electronics
Volume of the journal: 12
Issue Number: 8
Collation: 18 Seiten
DOI: 10.26083/tuprints-00023791
Corresponding Links:
Origin: Secondary publication DeepGreen

The increase in cyber-attacks impacts the performance of organizations in the industrial sector, exploiting the vulnerabilities of networked machines. The increasing digitization and technologies present in the context of Industry 4.0 have led to a rise in investments in innovation and automation. However, there are risks associated with this digital transformation, particularly regarding cyber security. Targeted cyber-attacks are constantly changing and improving their attack strategies, with a focus on applying artificial intelligence in the execution process. Artificial Intelligence-based cyber-attacks can be used in conjunction with conventional technologies, generating exponential damage in organizations in Industry 4.0. The increasing reliance on networked information technology has increased the cyber-attack surface. In this sense, studies aiming at understanding the actions of cyber criminals, to develop knowledge for cyber security measures, are essential. This paper presents a systematic literature research to identify publications of artificial intelligence-based cyber-attacks and to analyze them for deriving cyber security measures. The goal of this study is to make use of literature analysis to explore the impact of this new threat, aiming to provide the research community with insights to develop defenses against potential future threats. The results can be used to guide the analysis of cyber-attacks supported by artificial intelligence.

Uncontrolled Keywords: artificial intelligence, cyber security, industry 4.0, machine learning, deep learning
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-237918
Additional Information:

This article belongs to the Special Issue AI in Cybersecurity

Classification DDC: 000 Generalities, computers, information > 004 Computer science
300 Social sciences > 330 Economics
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 670 Manufacturing
Divisions: 16 Department of Mechanical Engineering > Product Life Cycle Management (PLCM)
Date Deposited: 12 May 2023 08:13
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23791
PPN: 50990338X
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