Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures

Sarker, Iqbal H.; Janicke, Helge; Ferrag, Mohamed Amine; Abuadbba, Alsharif

doi:10.1016/j.iot.2024.101110

Cited by 3 publications

(1 citation statement)

References 93 publications

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“…To effectively analyze the literature on using AI for cybersecurity, it is necessary to have a universally agreed-upon and integrated classification system due to the diversity of AI and cybersecurity . Th This systematic classification system will aid scholars and professionals in achieving a mutual understanding of the particular methodologies and utilities that require enhancement through the utilization of artificial intelligence for the successful execution of robust cybersecurity measures (Sarker et al, 2024).…”

Section: Research Gapmentioning

confidence: 99%

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

Faraji,

Shikder,

Hasan

et al. 2024

Intl. J. Rel.

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Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen their security posture against various security issues and cyberattacks. This objective focuses on analysing how AI-based cyber security (CS) solutions improve performance in financial transactions and banking sectors. It also aims to identify the latest advancements in AI-driven CS) research to enhance security and operational efficiency in the financial sector. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity in financial transactions. The review resulted in 800 studies, of which 225 articles remain. This paper will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in financial transactions. The review also identifies future research opportunities in examining cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for successful adoption in financial transactions. The paper might increase cyber security systems’ performance by increasing their defence against cyberattacks. Artificial intelligence approaches improve and enhance cyber security with machine learning and deep learning, fraud and threat detection, and this makes sure a secure and safe financial transaction. This study helps identify how to make transactions safer with cyber security. This study highlights the vital role of evaluation and continuous adaptation in AI. In the near future, this topic should focus on more collaboration among AI, cyber security, and system developers for better and secured outcomes.

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Section: Research Gapmentioning

confidence: 99%

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

Faraji,

Shikder,

Hasan

et al. 2024

Intl. J. Rel.

View full text Add to dashboard Cite

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Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects

Sarker,

Janicke,

Mohsin

et al. 2024

ICT Express

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Generative AI for Threat Intelligence and Information Sharing

Sindiramutty,

V. Prabagaran,

Jhanjhi

et al. 2024

Advances in Digital Crime, Forensics, and Cyber Terrorism

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Collaboration in providing threat intelligence and disseminating information enables cyber security professionals to embrace digital security most successfully, whose risks are ever-changing. This article dwells on the capacity of machine intelligence to change information security by categorising indicators of compromise (IOC) and threat actors, then highlights the limits of traditional methods. Among Artificial intelligence tools such as generative adversarial networks (GANs) and Variational autoencoders (VAEs), which are the key innovators, one can create synthetic or fake threat data that emulates real attack scenarios in the past. This allows cyber-related risks to be analysed differently from before. In addition, this feature enables secure stakeholder collaborations. It is also meant mainly for factual data that protects private information but allows the exchange of helpful information. It is clear from the fact that showcasing real-world examples demonstrates Al's automation through cybersecurity detection.

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Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures

Cited by 3 publications

References 93 publications

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects

Generative AI for Threat Intelligence and Information Sharing

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