A FRAMEWORK FOR SECURE IOT NETWORK MANAGEMENT IN SMART CITIES OPTIMIZING SECURITY AND SCALABILITY

Abstract

The rapid proliferation of Internet of Things (IoT) devices in smart cities has revolutionized urban management but introduced significant cybersecurity challenges. This article presents a novel framework for secure and efficient IoT network management, integrating an Enhanced Elliptic Curve Cryptography (EECC) model with machine learning (ML) for cyber-attack detection to optimize Quality of Service (QoS). Motivated by the vulnerabilities in existing smart city infrastructures, such as data breaches and scalability limitations, the research aims to propose guidelines for embedding EECC into IoT ecosystems while ensuring adaptability to future technologies. The methodology combines quantitative simulations, experimental validation using NS-3, and qualitative assessments of scalability. Achievements include a 30% reduction in computational overhead compared to traditional ECC and a 95% attack detection rate. Limitations involve the framework’s dependency on high computational resources for ML training and potential interoperability issues with legacy systems. This work provides a robust foundation for secure, scalable smart city IoT deployments.

The graphical abstract visually represents the proposed framework for secure IoT network management in smart cities. It features a central hexagonal node symbolizing the IoT ecosystem, surrounded by interconnected nodes depicting smart city components (traffic systems, energy grids, public safety). A shield overlay signifies the EECC-based security layer, with green lines indicating encrypted data flows. A neural network icon in the background highlights ML-driven cyber-attack detection. Performance metrics, such as a bar chart showing reduced latency and a pie chart illustrating attack detection accuracy, are integrated at the bottom. The color scheme uses blue for technology, green for security, and orange for performance, ensuring clarity and engagement.