Strengthening IoT Security with Machine Learning-Powered Intrusion Detection Systems for Critical Infrastructure and Smart Cities

Security of interdependent systems is becoming an increasingly pressing issue with the rapid expansion of IoT deployment in smart cities and critical infrastructure. Important services like transportation, healthcare, and energy management are at risk when cybercriminals target Internet of Things (IoT) equipment. One potential remedy to these vulnerabilities is the rise of Intrusion Detection Systems (IDS) that are powered by Machine Learning (ML). To better protect vital infrastructure and smart cities, this study investigates how to use machine learning methods with Internet of Things-Based intrusion detection systems. In order to identify and react to suspicious activity that may indicate security breaches like hacking, data theft, or unauthorized access, machine learning algorithms examine device behaviors, system logs, and network traffic patterns. The research looks at how several ML models, such as supervised (like Random Forests and Support Vector Machines) and unsupervised (like K-Means Clustering and Anomaly Detection), are used to construct strong IDS systems for Internet of Things settings. The study demonstrates how these algorithms can improve their detection accuracy with time by learning from massive amounts of data. In addition, it highlights the difficulties of incorporating IDS into IoT networks, including issues like resource limitations, the necessity for real-time processing, and the requirement to balance false positive and false negative rates. Through a comparative comparison, the study showcases the capabilities of IDS powered by machine learning in securing smart cities and vital infrastructure in a way that is both preemptive and scalable, making them more resistant to cyber threats as they evolve.