IoT cryptography with privacy-preserving ElGamal public key cryptosystem using oppositional artificial flora optimization algorithm

Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity, location, contact numbers, and energy utilization, security, and privacy become crucial. The lack of well-defined IoT-enabled security and privacy schemes will prevent the applicability of the IoT technologies from the client side. Therefore, this paper presents an ElGamal public key cryptosystem (EGPKC) for generating optimal keys utilizing an Oppositional Artificial Flora Optimization (EGPKC-OAFA) technique intended for preserving privacy in IoT. Additionally, the EGPKC approach with an optimum generation of authentication keys is applied on the medium access control (MAC) header for secure authentication. Besides, the optimal key selecting process for EGPKC occurs by employing the OAFA approach which inherits the oppositional-based learning (OBL) concept and classical artificial flora approach. To evaluate the effective analysis of the EGPKC-OAFA algorithm, huge simulations such as Known plaintext attack (KPA), chosen-plaintext attack (CPA), key sensitivity, and correlation coefficient are employed and the result is determined by different feature intervals. Accordingly, KPA and CPA reached the minimum value of 0.0987 and -0.0499 respectively. As a result, the proposed EGPKC-OAFA algorithm obtained superior performance compared to other methods.