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Journal of Cybersecurity and Information Management
Volume 9 , Issue 2, PP: 51-59 , 2022 | Cite this article as | XML | Html |PDF

Title

Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach

  Ahmed Sleem 1 * ,   Ibrahim Elhenawy 2

1  Ministry of communication and information technology, Egypt
    (Ahmedsleem8000@gmail.com)

2  Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt Emails: Ahmedsleem8000@gmail.com; ielhenawy@zu.edu.eg
    (ielhenawy@zu.edu.eg)

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Doi   :   https://doi.org/10.54216/JCIM.090205

Received: January 23, 2022 Accepted: April 01, 2022

Abstract :

This paper proposes a privacy-preserving federated learning approach to enhance cyber threat intelligence sharing. Cyber threats are becoming more sophisticated and are posing serious security risks to organizations. Sharing threat intelligence information can help to detect and mitigate these threats quickly. However, privacy concerns and data protection regulations hinder the sharing of sensitive information. Federated learning is a promising approach that allows multiple parties to collaborate in building a global model while preserving data privacy. We propose a framework that utilizes federated learning to train a global threat intelligence model without compromising the privacy of individual organizations' data. Our approach also includes a differential privacy mechanism to ensure the anonymity of the participating organizations. We demonstrate the effectiveness of our approach through experiments conducted on real-world datasets, showing that it achieves high accuracy while maintaining data privacy. The proposed approach has the potential to facilitate more effective and secure cyber threat intelligence sharing among organizations.

Keywords :

Cyber Threat; Privacy-Preserving Federated Learning; Intelligent Systems; Cybersecurity

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Cite this Article as :
Style #
MLA Ahmed Sleem, Ibrahim Elhenawy. "Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach." Journal of Cybersecurity and Information Management, Vol. 9, No. 2, 2022 ,PP. 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
APA Ahmed Sleem, Ibrahim Elhenawy. (2022). Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach. Journal of Journal of Cybersecurity and Information Management, 9 ( 2 ), 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
Chicago Ahmed Sleem, Ibrahim Elhenawy. "Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach." Journal of Journal of Cybersecurity and Information Management, 9 no. 2 (2022): 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
Harvard Ahmed Sleem, Ibrahim Elhenawy. (2022). Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach. Journal of Journal of Cybersecurity and Information Management, 9 ( 2 ), 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
Vancouver Ahmed Sleem, Ibrahim Elhenawy. Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach. Journal of Journal of Cybersecurity and Information Management, (2022); 9 ( 2 ): 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
IEEE Ahmed Sleem, Ibrahim Elhenawy, Enhancing Cyber Threat Intelligence Sharing through a Privacy-Preserving Federated Learning Approach, Journal of Journal of Cybersecurity and Information Management, Vol. 9 , No. 2 , (2022) : 51-59 (Doi   :  https://doi.org/10.54216/JCIM.090205)
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