Methods for Securing IoT Devices Using Machine Learning and Deep Learning: An Evaluation

With forecasts of 50 billion connected devices by 2021, the Internet of Things is one of the fastest-growing fields in computer science. There is no need for human intervention thanks to the IoT, which links billions of smart devices together so they may talk to one another. However, the interconnected nature of IoT devices and the deployment components has raised new security challenges in encryption, identification, security systems, and network and vulnerability assessment. The focused learning of the Transfer learning-Machine learning-deep learning (TL-ML/DL) framework and its applications to machine learning are examined in detail here. Several attack surfaces for IoT systems for security (IoT-SS) and the risks of using those surfaces are discussed. To ensure the safety of IoT devices, researchers are looking at the potential benefits of machine learning and deep learning. The simulation study is performed according to the safety, efficiency, and performance criteria, demonstrating the suggested framework's dependability. Results from the simulations are analyzed and compared to preexisting models in terms of accuracy 98%, precision 97%, sensitivity 92%, specificity 98%, etc. The proposed IoT-SS system can achieve better simulation results thanks to deep learning and machine learning.