Enhanced IDS with Deep Learning for IoT-Based Smart Cities Security

Abstract

Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for an extended period. IoT interface attacks must be evaluated in real-time for effective safety and security measures. This study implements a smart intrusion detection system (IDS) designed for IoT threats, and interoperability with IoT connectivity standards is offered by the identity solution. An IDS is a common type of network security technology that has recently received increasing interest in the research community. The system has already piqued the curiosity of scientific and industrial communities to identify intrusions. Several IDSs based on machine learning (ML) and deep learning (DL) have been proposed. This study introduces IDS-SIoDL, a novel IDS for IoT-based smart cities that integrates long shortterm memory (LSTM) and feature engineering. This model is tested using tensor processing unit (TPU) on the enhanced BoT-IoT, Edge-IIoT, and NSL-KDD datasets. Compared with current IDSs, the obtained results provide good assessment features, such as accuracy, recall, and precision, with approximately 0.9990 recording time and calculating times of approximately 600 and 6 ms for training and classification, respectively.