Recognition of Attacks on IoT Devices and Their Prevention Using Deep Learning Methods

The Internet of Things (IoT) has caused a revolution in many different industries by making it possible for connected smart devices to communicate with one another effortlessly. However, due to the growing number of potential access points for assaults, this interconnectivity also presents substantial issues for the maintenance of information security. This research article investigates the application of deep learning methods to identify and prevent attacks against the IoT. We give a comprehensive examination of ANN and DNN based on deep learning to improve the security and resilience of Internet of Things networks. The effectiveness of the suggested model is evaluated in this paper through tests conducted on data taken from the real world, revealing the ability of the model to protect IoT devices from being compromised by cyberattacks. Accuracy, precision, recall, and f1 score are the measures of evaluation that are utilized in the process of comparing and contrasting performance. The system passed the DNN accuracy test with a score of 99%.