Deep Learning Based Intrusion Detection System with Integration of Blockchain Technology and Internet of Things

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, Rivest, Shamir, and Adleman (RSA) algorithm for encryption. and Deep Neural Network (DNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models.