Moderation of cyber attacks in IoTs using deep learning techniques

The widespread use of IoT devices in the modern day has simplified our lives and elevated our everyday routines to a new level.IoT devices are connected to communicate and share data with gateways or access points (APs) for additional data processing.On the other hand, this makes cybersecurity and zero-day assaults in IoT networks more prevalent.Deep learning models and datasets used to identify fraudulent data in IoT environments have been reviewed in this research.In the context of the Internet of Things, we found that the Long Short-Term Memory (LSTM), Convolution Neural Network (CNN) and stacking auto-encoders improve the accuracy and precision of malicious packet detection.We undertook a thorough theoretical examination of deep learning datasets and models.Our research finding serves as paradigm to researchers as a technique to investigate IoT security and privacy challenges.