Fostering Collaborative Threat Intelligence Sharing for Enhanced Cloud Security using AegisNet

Abstract

The growing adoption of cloud computing has revolutionized data storage and processing but also introduced significant security challenges, including advanced persistent threats, data breaches, and sophisticated cyberattacks. Traditional isolated security measures often fall short in combating these dynamic threats. Collaborative threat intelligence sharing emerges as a pivotal solution, fostering real-time exchange of security insights among organizations, cloud service providers, and threat researchers. This paper emphasizes the development of a robust framework for secure, efficient, and privacy-preserving threat intelligence sharing in cloud environments. Leveraging advanced techniques such as blockchain for immutable data logging, homomorphic encryption for privacy-preserving computations, and machine learning for predictive analytics, the framework ensures timely and actionable intelligence dissemination without exposing sensitive data. By integrating standardized protocols and incentivizing participation, the proposed approach addresses barriers such as trust deficits, data privacy concerns, and resource disparities among stakeholders. Case studies and simulations demonstrate the framework’s effectiveness in detecting and mitigating emerging threats, reducing incident response times, and enhancing overall security resilience. The findings underscore the transformative potential of collaborative threat intelligence sharing in creating a proactive and adaptive cloud security ecosystem, fostering trust and cooperation across diverse entities. This initiative aligns with the critical need for collective defense mechanisms in an era of increasing cyber threats, paving the way for a more secure digital landscape.Top of FormBottom of Form