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Articles

Vol. 1 No. 1 (2021): Cybersecurity and Network Defense Research (CNDR)

Threat Intelligence Sharing: Analyzing Strategies and Challenges in Sharing Threat Intelligence Among Organizations to Enhance Cybersecurity Posture and Incident Response

Published
25-07-2024

Abstract

Threat intelligence sharing plays a crucial role in enhancing cybersecurity posture and incident response capabilities. This paper provides an in-depth analysis of the strategies and challenges associated with sharing threat intelligence among organizations. The study examines various approaches to threat intelligence sharing, including information sharing and analysis centers (ISACs), public-private partnerships, and industry collaboration initiatives. The paper also explores the benefits of threat intelligence sharing, such as improved situational awareness, faster incident response, and enhanced defense against cyber threats. However, several challenges hinder effective threat intelligence sharing, including trust issues, legal and regulatory concerns, and technical interoperability issues. The paper concludes with recommendations for addressing these challenges and enhancing the effectiveness of threat intelligence sharing efforts.

References

  1. Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
  2. Raparthi, Mohan, Sarath Babu Dodda, and SriHari Maruthi. "Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks." European Economic Letters (EEL) 10.1 (2020).
  3. Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
  4. Vyas, Bhuman. "Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance." Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal 10.1 (2021): 59-62.
  5. Rajendran, Rajashree Manjulalayam. "Scalability and Distributed Computing in NET for Large-Scale AI Workloads." Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal 10.2 (2021): 136-141.
  6. Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
  7. Raparthi, M., Dodda, S. B., & Maruthi, S. (2020). Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks. European Economic Letters (EEL), 10(1).
  8. Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
  9. Vyas, B. (2021). Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(1), 59-62.
  10. Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
  11. Rajendran, R. M. (2021). Scalability and Distributed Computing in NET for Large-Scale AI Workloads. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(2), 136-141.
  12. Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.