Detection and Prevention of Cyber Threats in Smart Cities Using Machine Learning and Intrusion Detection Systems

The increasing interconnectedness of smart cities has led to a surge in cyber threats. This research investigates the application of machine learning (ML) techniques to bolster the security of smart city environments. A comprehensive systematic literature review was conducted to analyze the effectiveness of various ML methodologies in detecting and preventing cyberattacks, including intrusions, malware, and spam. The study highlights the crucial role of ML in strengthening the security infrastructure of smart cities. By combining quantitative and qualitative research methods, this study explores the impact of ML-based intrusion detection systems on network traffic, expert opinions, and user interactions. The analysis involves employing statistical techniques, mathematical modeling, and ML algorithms to identify patterns and trends in the data. The findings of this research contribute to the advancement of cybersecurity in smart cities, enabling the development of more robust and resilient defense mechanisms.