Edge-Based IoT Security Using Lightweight Cryptography and Machine Learning for Smart City Communication Systems

Smart city communication systems increasingly depend on Internet of Things (IoT) devices for real-time monitoring, automation, traffic management, smart healthcare, intelligent surveillance, and energy optimisation. However, IoT networks remain highly vulnerable due to limited computing resources, large-scale device heterogeneity, insecure communication channels, and the growing sophistication of cyberattacks. Traditional cloud-centric security frameworks often introduce high latency, bandwidth overhead, and increased exposure to threats. To overcome these challenges, edge-based security solutions are gaining prominence by enabling real-time protection closer to the data source. This research paper proposes an integrated edgebased IoT security model that combines lightweight cryptography for secure communication and machine learning (ML) for anomaly and intrusion detection in smart city environments. The system design is evaluated using a simulated smart city IoT network with varied attack scenarios including distributed denial of service (DDoS), spoofing, botnet infiltration, and data manipulation. Statistical analysis demonstrates that the integrated model significantly improves detection performance while maintaining low computational overhead, making it suitable for resource-constrained devices. The results indicate strong improvement in accuracy, reduced latency, and enhanced resilience against major IoT threats. This work provides a scalable and efficient security framework for next-generation smart city communication systems by merging cryptographic integrity and intelligent edge monitoring.