A Cryptography and Machine Learning Based Authentication for Secure Data-Sharing in Federated Cloud Services Environment

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.

Keywords:

• Elliptic curve cryptography
• mutual authentication
• ProVerif
• Schnorr signature
• Voting Classifier

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This research work is financially supported by National Institute of Technology Kurukshetra, India.