An Identity-Based Secure and Optimal Authentication Scheme for the Cloud Computing Environment

Security is a critical issue in cloud computing (CC) because attackers can fabricate data by creating, copying, or deleting data with no user authorization. Most of the existing techniques make use of password-based authentication for encrypting data. Password-based schemes suffer from several issues and can be easily compromised. This paper presents a new concept of hybrid metaheuristic optimization as an identity-based secure and optimal authentication (HMO-ISOA) scheme for CC environments. The HMOISOA technique makes use of iris and fingerprint biometrics. Initially, the HMO-ISOA technique involves a directional local ternary quantized extrema pattern-based feature extraction process to extract features from the iris and fingerprint. Next, the features are fed into the hybrid social spider using the dragon fly algorithm to determine the optimal solution. This optimal solution acts as a key for an advanced encryption standard to encrypt and decrypt the data. A central benefit of determining the optimal value in this way is that the intruder cannot determine this value. The attacker also cannot work out which specific part of the fingerprint and iris feature values are acted upon as a key for the AES technique. Finally, the encrypted data can be saved in the cloud using a cloud simulator. Experimental analysis was performed on five fingerprint and iris images for a man-in-the-middle attack. The simulation outcome validated that the presented HMO-ISOA model achieved better results compared with other existing methods.