Cybersecurity in Internet of Things Networks using Deep Learning Models

Abstract

The Internet of Things and global grid that connects millions of sensors and devices have made distributed software and services ubiquitous. IoT’s vast reach and economic value make it a natural target for hackers, making security the industry’s top worry. Privacy has always been studied, but IoT design and new intimidations make most standard safeguards ineffective. Deep learning’s data, odd case technique, and ability to detect unexpected threats may help detect IoT intrusions. The “Internet of Things” includes computers, sensors, network infrastructure, software, networks, users, gateways, and services (IoT). Due to the increasing importance of Internet of Things (IoT) innovations, growth and management are huge. Data protection and privacy have recently caught users’ attention. As social media grows, more people connect. As connections grow, more safe spaces are needed. This article discusses data protection, including deep learning models, Machine Learning (ML) concepts, safety and security, and cybersecurity management. To illustrate IoT network vulnerability, convolutional neural networks (CNN), Long Short-Term Memory (LS TM), and CNN combinations have been studied and compared. We can detect IoT cyberattacks with over 99% accuracy using Modbus network traffic data.