AI-Based Holistic Framework for Cyber Threat Intelligence Management

被引:0
|
作者
Spyros, Arnolnt [1 ]
Koritsas, Ilias [1 ]
Papoutsis, Angelos [1 ]
Panagiotou, Panos [1 ]
Chatzakou, Despoina [1 ]
Kavallieros, Dimitrios [1 ]
Tsikrika, Theodora [1 ]
Vrochidis, Stefanos [1 ]
Kompatsiaris, Ioannis [1 ]
机构
[1] Ctr Res & Technol Hellas, Informat Technol Inst, Thessaloniki 57001, Greece
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Artificial intelligence; cyber threat intelligence; data classification; data correlation; honeypots; machine learning; named entity recognition; outlier detection; social media crawling; web crawling; WEB; TEXT;
D O I
10.1109/ACCESS.2025.3533084
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cyber Threat Intelligence (CTI) is an important asset for organisations to facilitate the safeguarding of their systems against new and emerging cyber threats. CTI continuously provides up-to-date information which enables the design and implementation of better security measures and mitigation strategies. Organisations gather data from different sources either internal or external to the organisation, which are analysed, resulting in CTI. Nevertheless, the gathered data usually contain a large amount of content that is irrelevant to CTI or even to cybersecurity. Furthermore, most approaches concerning CTI management (e.g., gathering, analysis) involve simply gathering and storing the information without any enrichment such as classification or correlation. However, in order to obtain optimal results, organisations should be able to utilise all capabilities of CTI. Therefore, in this work, we propose ThreatWise AI, a novel framework that enables the gathering, analysis, enrichment, storage, and sharing of CTI in an efficient and secure manner. In particular, we have developed a novel pipeline in ThreatWise AI which incorporates different advanced tools, with distinct capabilities that interact with each other to provide a complete set of functionalities for the administration of the overall CTI lifecycle. The developed tools integrate various Python scripts and provide gathering and analysis functionalities of CTI. Furthermore, the proposed framework leverages the MISP platform for storing, enriching and sharing while also integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms for advanced data enrichment.
引用
收藏
页码:20820 / 20846
页数:27
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