EGCrypto: A Low-Complexity Elliptic Galois Cryptography Model for Secure Data Transmission in IoT

Abstract

In recent years, data security has been a challenging endeavor, especially when the data is being transmitted every second. Internet of Things (IoT) involves continuously sending data over public networks, making the data vulnerable to various security threats. Therefore, ensuring the secure end-to-end communication of IoT data is critical. Cryptography and steganography have proven effective in providing secure connectivity for IoT devices. However, challenges in existing approaches include scalability, computational complexity, implementation, key management, trade-offs, evolving threats, and hyperparameter tuning. Therefore, in this paper, we propose EGCrypto, an efficient and secure model for IoT networks. EGCrypto utilizes a low-complexity elliptic Galois cryptography approach along with matrix XOR steganography to enhance security. To optimize its performance, we employ the zoning evolution of control attributes and adaptive mutation based self-adaptive differential evolution with fitness and diversity ranking. These techniques are utilized to fine-tune the hyperparameters of EGCrypto, enhancing its effectiveness and efficiency. The confidential IoT data is encrypted using low-complexity elliptic Galois cryptography. Following encryption, the encrypted data is embedded or hidden into cover blocks of an image, which are selected using the optimization algorithm. This ensures secure data communication in IoT architectures, as the encrypted data is transferred safely and can be easily recovered and decrypted at the receiving end. The experimental results demonstrate that EGCrypto outperforms competitive models with improvements of 1.8473% in peak signal to noise ratio (PSNR), 1.5490% in strutural similarity index metric (SSIM), 1.7682% in normalized root mean square error (NRMSE), 1.3829% in carrier capacity, and 1.9372% in embedding efficiency.