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Study on Hybrid Cloud-based Cyber Threat Intelligence Sharing Model Requirements Analysis

Published: 27 September 2021 Publication History

Abstract

Because of the phenomenal growth of internet connectivity and unceasing improvements over hardware and software technology, everything is connected without any boundaries creating the Internet of Things, which becomes part of cyberspace. As the volume of cyberspace is overgrowing, the attackers' capabilities and strategies to find new vulnerabilities are increasing too. In order to defend the attackers from stealing and poisoning critical information of cyberspace, it is crucial to implement Cyber Threat Intelligence (CTI). However, it is not a straightforward process to implement CTI for individuals and organizations because of the resource constraints such as time, capability, and cost. Hence, different stakeholders (individuals or organizations) can collaborate and create a CTI sharing platform to improve their security stances and other organizations. It is well-known that when connected devices are rapidly intensifying in IoT, the cyber threat to information will also rise. So, to own a reliable, scalable, and high-speed CTI sharing platform, the deployment model should be wisely studied. This research carefully analyzed different forms of CTI sharing deployment models: private cloud, public cloud, hybrid cloud, and on-premises. Finally, we suggest the hybrid cloud-based deployment model as a convenient solution for CTI sharing due to its desirable features such as scalability, security, reliability, and high speed.

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  • (2025)Literature Review of Machine Learning and Threat Intelligence in Cloud SecurityIEEE Access10.1109/ACCESS.2025.352963613(11663-11678)Online publication date: 2025

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cover image ACM Conferences
ACM ICEA '20: Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications
December 2020
219 pages
ISBN:9781450383042
DOI:10.1145/3440943
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 September 2021

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Author Tags

  1. CTI
  2. Cloud
  3. Cyber Threat
  4. Cyber Threat Intelligence
  5. Hybrid Cloud
  6. Intelligence
  7. Sharing Model

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  • Refereed limited

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  • Defense Acquisition Program Administration and Agency for Defense Development

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ACM ICEA '20
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  • (2025)Literature Review of Machine Learning and Threat Intelligence in Cloud SecurityIEEE Access10.1109/ACCESS.2025.352963613(11663-11678)Online publication date: 2025

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