Generative AI for Threat Intelligence and Information Sharing

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Author(s): Siva Raja Sindiramutty (Taylor's University, Malaysia), Krishna Raj V. Prabagaran (Universiti Malaysia Sarawak, Malaysia), N. Z. Jhanjhi (Taylor's University, Malaysia), Raja Kumar Murugesan (Taylor's University, Malaysia), Sarfraz Nawaz Brohi (University of the West of England, UK)and Goh Wei Wei (Taylor's University, Malaysia)
Copyright: 2025
Pages: 44
Source title: Utilizing Generative AI for Cyber Defense Strategies
Source Author(s)/Editor(s): Noor Zaman Jhanjhi (Taylor's University, Malaysia)
DOI: 10.4018/979-8-3693-8944-7.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

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Abstract

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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Generative AI for Threat Intelligence and Information Sharing

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