Elliptic Curve Diffie-Hellman Random Keys Using Artificial Neural Network and Genetic Algorithm for Secure Data over Private Cloud
Othman Alesawy, Ravie Chandren Muniyandi · Information Technology Journal · 2016
Background: Achieving secure communication and safeguarding sensitive data from unauthorized access over public networks are major concerns in cloud servers.Unique and random encryption keys are vital for data security.Methodology: A public key cannot be derived from a random key generator because it can allow middlemen to attack the network easily and access sensitive data.Hence, to secure both data and keys, a system should be used to generate intermediate encryption and decryption keys that are unique, mixed and random.This study investigates how much time is needed to encrypt and decrypt the Elliptic Curve Diffie-Hellman (ECDH) key between cloud users and cloud servers, which are simulated as GUI tools.Results: Findings showed that the time consumed increases as the number of text files grows.Conclusion: Thus, this experiment demonstrates good improvement in time when an Artificial Neural Network (ANN) is applied to ECDH key exchanges.When the developed ECDH with ANN is applied to genetic algorithms, a high efficiency in terms of the time consumed, performance and accuracy is achieved.