An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning

Abstract

The Internet of Things (IoT) is expanding rapidly with billions of connected devices worldwide, necessitating robust security solutions to protect these systems. This paper proposes a comprehensive and adaptive security framework called Enhanced Secure Channel Authentication using random forests and software-defined networking (SCAFFOLD), tailored for IoT environments. The framework establishes secure communication channels between IoT nodes using software-defined networking (SDN) and machine learning techniques. The key components include encrypted channels using session keys, continuous traffic monitoring by the SDN controller, ensemble machine-learning for attack detection, precision mitigation via SDN reconfiguration, and periodic reauthentication for freshness. A mathematical model formally defines the protocol. Performance evaluations via extensive simulations demonstrate Enhanced SCAFFOLD’s ability to reliably detect and rapidly mitigate various attacks with minimal latency and energy consumption overheads across diverse IoT network scenarios and traffic patterns. The multidimensional approach combining encryption, intelligent threat detection, surgical response, and incremental hardening provides defense-in-depth to safeguard availability, integrity, and privacy within modern IoT systems while preserving quality of service.

Keywords

• Internet of Things
• IoT security
• software-defined networking
• machine learning
• attack detection