Implementing Deep Learning Algorithms for Solving Security Issues and Challenges in the IoT Network

The Internet of Things (IoT) has completely transformed to various sectors including healthcare, smart cities, and industrial automation enabling linkages of various devices and exchange of information in real-time. However, it has also posed new critical security and privacy issues such as malicious user access, data hijacking, and malicious hacking, which are hard and time consuming to deal with traditionally through use of rule-based security and cryptographic solutions. In this paper, this work proposed a new deep learning architecture which would help in overcoming these limitations using a combination of Conventional Neural Networks (CNN), and Long Short-Term Memory (LSTM) architecture to successfully extract spatial and time-related features to identify abnormalities and intrusions. This model incorporates both Federated Learning (FL) to preserve the privacy of the user via non-exchange of raw data and blockchain technology as one of the highest levels of authentication to identify and verify that the identity has not been disturbed. The model was tried with actual IoT datasets, i.e., NSL-KDD, UNSW-NB15 and CICIDS2017, and operate on an edge computing platform as a means of presenting low latency applications. Experimental data indicates the superiority of the proposed model as they achieve the accuracy of 98.4, the rates of false positives equal to 2.1, and the latency rates of detection of 2.3 milliseconds. The research underlined the resilience, scalability and privacy protection of the AI-based security systems and this is the reason it can be perceived to be the solution to the security of future IoT environment against the complicated cyber-attack.