A Detailed Comparative Study of AI-Based Intrusion Detection System for Smart Cities

The proliferation of Internet of Things (IoT) devices poses potential challenges in the fast-developing field of smart cities, especially in cybersecurity. This work is an attempt to present an extensive comparative analysis of AI-based intrusion detection systems (IDS) designed for smart cities. The necessity of robust intrusion detection systems is highlighted early on in discussing the security threats associated with IoT and its importance in enhancing urban life. The study looks at current datasets that are frequently utilized to train IDS models, emphasizing both their shortcomings and the growing need for more diverse and representative datasets. This work explores the use of deep learning (DL) and machine learning (ML) approaches for intrusion detection in smart city environments, it looks at different classifiers and how well they identify cyber threats. This paper presents simulation methods and experimental results for demonstrating the accuracy, precision, recall, and F1-score performance of many classifiers using the complex datasets NF_BoT_IoT and IoTID20. This paper also touches upon the significance of continuing research in utilizing cutting-edge algorithms and novel datasets to improve smart cities' cybersecurity posture and, eventually, aid in creating more complex intrusion detection systems that can successfully handle the ever-changing threat landscape. It also emphasizes the necessity of ongoing innovation in this field and the potential for deep learning algorithms to improve cyberattack detection skills.