Abstract

Secure mutual authentication is an indispensable requirement to share organizational invaluable data among collaborating entities in federated cloud environment. This work presents a novel mutual authentication method which incorporates machine learning based ensemble Voting Classifier for online threat detection and Elliptic Curve Cryptography with Schnorr’s signature scheme based key agreement to ensure secure communication among the participating entities by prior detection and mitigation of security breaches. The performance evaluation by using a benchmark from Canadian Institute for Cybersecurity Datasets and ProVerif security analysis tool verifies its efficiency in terms of security features and communication cost over existing approaches.

摘要

安全的相互身份验证是在联合云环境中的协作实体之间共享组织宝贵数据的必不可少的要求。这项工作提出了一种新颖的相互身份验证方法，该方法将基于机器学习的集成投票分类器用于在线威胁检测和椭圆曲线密码学与 Schnorr 基于签名方案的密钥协议相结合，以通过事先检测和缓解安全漏洞来确保参与实体之间的安全通信。通过使用加拿大网络安全数据集研究所和 ProVerif 安全分析工具的基准进行的性能评估，验证了其在安全功能和通信成本方面的效率超过现有方法。