ABSTRACT
The swift proliferation of Internet of Things (IoT) devices into smart cities with control systems and critical infrastructure are examined as significant constituents in the designing of smart cities. The network traffic of a smart city operates in real-world time via IoT objects. These IoT objects are being associated with sensors that are mounting exponentially and initiating new cybersecurity challenges. The objective is to make an essential cyber system design to alleviate IoT-related security threats and attacks that exploit security vulnerabilities. As a result, Intrusion Detection Systems (IDS) have become a decisive component in network security for stringent performance and scalability requirements on modern data acquisition as well as supervisory control systems. IoT is turning as a key source of producing vast amounts of information, flowing through many connected nodes. This makes an essential demand of IDS designed for smart city environments to alleviate IoT-related security threats and attacks that exploit security vulnerabilities. This chapter focuses on the development of a deep learning (DL)-based security model for IoT device networks to meet the essential security vulnerabilities. The simulation model results state that the DL neural network performs effectively well in categorizing the attacks.
