A Comprehensive Study of Intrusion Detection within Internet of Things-based Smart Cities: Synthesis, Analysis and a Novel Approach

In order to improve the quality of human existence, comfort and efficiency are key objectives in smart environments. It is now possible to construct smart cities due to the latest advancements in Internet of Things (IoT) technology. Privacy and security are major concerns in IoT-based smart objects. Smart environments are at risk for safety from IoT-based technologies. Intrusion detection systems (IDSs) created for IoT environments are essential for preventing IoT-related security threats. Many cyber security systems use IDSs to find intrusions. Anomaly-based IDS learns the typical pattern of system activity and alerts on anomalous events as they happen as opposed to analyzing monitored events against a database of known intrusion events, as is the case with signature-based IDS. The installation of IDS on the IoT network is the main topic of this paper. Key design approach presented in this paper must be taken into consideration when developing an intrusion detection system for the Internet of Things. In this study, we use the Convolutional Neural Network (CNN) to identify attacks on nine commercial IoT devices. Using an actual N-BaIoT dataset that was taken from a real system and included both benign and harmful patterns, extensive empirical research was conducted. The testing results demonstrated a good accuracy of the CNN model in identifying botnet assaults from security cameras with accuracies of 90.25% and 91.76%. Overall, the CNN model was effective in accurately identifying botnet attacks from a variety of IoT devices.