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A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience.

作者信息

Saeed Saqib, Suayyid Sarah A, Al-Ghamdi Manal S, Al-Muhaisen Hayfa, Almuhaideb Abdullah M

机构信息

SAUDI ARAMCO Cybersecurity Chair, Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

出版信息

Sensors (Basel). 2023 Aug 19;23(16):7273. doi: 10.3390/s23167273.


DOI:10.3390/s23167273
PMID:37631808
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10459806/
Abstract

Cybersecurity is a significant concern for businesses worldwide, as cybercriminals target business data and system resources. Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain. This research investigates how companies can employ CTI to improve their precautionary measures against security breaches. The study follows a systematic review methodology, including selecting primary studies based on specific criteria and quality valuation of the selected papers. As a result, a comprehensive framework is proposed for implementing CTI in organizations. The proposed framework is comprised of a knowledge base, detection models, and visualization dashboards. The detection model layer consists of behavior-based, signature-based, and anomaly-based detection. In contrast, the knowledge base layer contains information resources on possible threats, vulnerabilities, and dangers to key assets. The visualization dashboard layer provides an overview of key metrics related to cyber threats, such as an organizational risk meter, the number of attacks detected, types of attacks, and their severity level. This relevant systematic study also provides insight for future studies, such as how organizations can tailor their approach to their needs and resources to facilitate more effective collaboration between stakeholders while navigating legal/regulatory constraints related to information sharing.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/acd4422e7b35/sensors-23-07273-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/222173d39af4/sensors-23-07273-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/2c50ef56db14/sensors-23-07273-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/acd4422e7b35/sensors-23-07273-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/222173d39af4/sensors-23-07273-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/2c50ef56db14/sensors-23-07273-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e158/10459806/acd4422e7b35/sensors-23-07273-g003.jpg

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引用本文的文献

[1]
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[2]
A Systematic Review of Cyber Threat Intelligence: The Effectiveness of Technologies, Strategies, and Collaborations in Combating Modern Threats.

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[3]
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Front Artif Intell. 2025-1-9

本文引用的文献

[1]
Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Sensors (Basel). 2022-4-28

[2]
ReOpen demands as public health threat: a sociotechnical framework for understanding the stickiness of misinformation.

Comput Math Organ Theory. 2022

[3]
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.

BMJ. 2021-3-29

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