A hybrid cryptographic and deep learning approach for IoT device security

A seamless network of connected things and automated processes have transformed a variety of industries thanks to the Internet of Things (IoT). In addition to introducing significant security vulnerabilities, IoT devices are also susceptible to cyberattacks due to their limited computing power. There is no doubt that traditional cryptographic techniques provide a foundation of security, but they are often inadequate to counter sophisticated attacks. A hybrid security framework is presented in this paper that combines enhanced Elliptic Curve Cryptography (ECC) algorithms with deep learning-based intrusion detection algorithms to address these challenges. Using an enhanced Elliptic Curve Cryptography (ECC) algorithm along with deep learning-based intrusion detection, this paper addresses these challenges. According to the performance evaluations, the proposed approach reduces encryption and decryption times, lowers memory consumption, and enhances processing efficiency. This hybrid cryptographic/deep learning approach improves IoT security, providing a scalable and reliable means of protecting IoT networks against emerging threats.