Implementation of A Deep Learning Framework for Intelligent Intrusion Detection in Internet of Things Networks

Internet of Things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of Things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for “smart” dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. This study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. This research work has proposed a novel deep-learning-based IoT invasion detection method. The cutting-edge Internet of Things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. The accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions.