IoT Based Smart Cities Application For Detecting Cyber-Attacks Using Machine Learning

Abstract

In recent years, the increased adoption of Internet of Things (IoT) apps has led to the emergence of smart cities. Smart cities are designed to improve the value of public services and the welfare of its citizens. To do this, they make use of IoT-enabled technology, communication, and applications. However, with increasing smart city networks, there is a greater chance of online security risks and attacks. IoT devices are especially vulnerable to such dangers and malicious attacks due to their connection with sensors and cloud servers. That’s why it is essential to develop measures to avoid such assaults and prevent IoT gadgets from breakdown.

This research proposes an IoT-based model for smart city applications to detect cyber-attacks by using machine learning. This proposed model performs better than the Deep Neural Network Expert System (DeNNeS) approach and provides more reliable and secure functionality to the power networks (Smart Traffic, Smart Grid & Smart Buildings) against cyber-attacks by using machine learning in the future.