artificial intelligence for cybersecurity literature review and future research directions

Artificial Intelligence for Cybersecurity: Literature Review and Future Research Directions

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 the security posture against various security issues and cyberattacks. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review resulted in 2395 studies, of which 236 were identified as primary. This article classifies the identified AI use cases based on a NIST cybersecurity framework using a thematic analysis approach. This classification framework will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in different contexts. The review also identifies future research opportunities in emerging cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for the successful adoption of AI-based cybersecurity in today's era of digital transformation and polycrisis.

Publisher URL: https://www.sciencedirect.com/science/article/pii/S1566253523001136

Open URL: https://doi.org/10.1016/j.inffus.2023.101804

DOI: 10.1016/j.inffus.2023.101804

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Computer Science > Cryptography and Security

Title: large language models for cyber security: a systematic literature review.

Abstract: The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.

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Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review

• Original - Survey or Exposition
• Published: 08 May 2024

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• Abd El Hedi Gabsi   ORCID: orcid.org/0000-0003-4925-2367 1  

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This review article explores the integration of artificial intelligence (AI) in industry 4.0 and its transformative impact on the manufacturing sector. The core principles of industry 4.0, revolving around digitalization, automation, and connectivity, are examined, emphasizing the creation of “smart factories”. The article also discussed the different categories of AI, such as narrow AI and general AI, and their significance in industry 4.0. The advantages of AI technologies in enhancing productivity, efficiency, and decision-making processes in manufacturing are discussed, supported by real-world case studies. In addition to the benefits, the article addresses the challenges and limitations of AI implementation. It delves into the current status of AI and Human Workforce Collaboration, highlighting the seamless integration of AI technologies with human workers to maximize efficiency in manufacturing. The article explores the innovation and customization of AI in industry 4.0. Moreover, the review addresses the future directions for AI implementation. By examining these key aspects, the article offers valuable insights into the transformative potential of AI in industry 4.0 and its implications for the future of manufacturing.

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A Conceptual Framework Involving Barriers in the Integration of Additive Manufacturing with Industry 4.0 Practices

Challenges in Implementation of Industry 4.0 in Manufacturing Sector

Artificial Intelligence

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Abbreviations

• Artificial intelligence
• Machine learning
• Deep learning

Computer vision

Internet of things

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Gabsi, A.E.H. Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06012-6

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