AI-Based Cyber Threat Intelligence: Automating Threat Discovery and Analysis

Varmaziyar, Mahdi

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Volume 4, Issue 9 (12-2025)

3 2025, 4(9): 36-41

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AI-Based Cyber Threat Intelligence: Automating Threat Discovery and Analysis

Mahdi Varmaziyar

Abstract: (67 Views)

In recent years, the exponential growth of cyber threats and the increasing complexity of attacks have highlighted the urgent need for developing automated, AI-based threat intelligence systems. This review paper examines the frameworks, methods, and key challenges in automating threat discovery and analysis processes, elucidating the role of machine learning, deep learning, and graph models in improving the accuracy and speed of threat detection. Results from a systematic literature review indicate that integrating multi-source data with deep learning models—such as CNNs, LSTMs, and GNNs—can enhance the detection accuracy of multi-stage attacks. Moreover, the application of Explainable AI (XAI) increases the transparency of model decisions, fosters trust among security analysts, and enables effective human–machine interaction in operational environments.

Keywords: Cyber Threat Intelligence, Artificial Intelligence, Automated Threat Detection, Explainable AI (XAI).

Full-Text [PDF 403 kb] (53 Downloads)

Type of Study: Research | Subject: Special
Received: 2025/10/26 | Accepted: 2025/12/1 | Published: 2025/12/1

References

1. M. Mashfiquer Rahman et al., “AI integration in cybersecurity software: Threat detection and response,” IEEE Access, 2025

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4. M. Ma et al., “ActMiner: Applying Causality Tracking and Increment Aligning for Graph-based Cyber Threat Hunting,” 2025

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Varmaziyar M. AI-Based Cyber Threat Intelligence: Automating Threat Discovery and Analysis. 3 2025; 4 (9) :36-41
URL: http://jiis.iauh.ac.ir/article-1-56-en.html

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	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Volume 4, Issue 9 (12-2025)

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