# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 5 Enhanced SCAFFOLD Protocol  
The Enhanced SCAFFOLD Protocol section presents the proposed security framework in detail. It describes the key features, mathematical model, protocol messages, and algorithms of Enhanced SCAFFOLD. This section highlights how the protocol integrates SDN, machine learning, and cryptographic techniques to provide comprehensive IoT security. This section covers the improved SCAFFOLD protocol. It establishes secure communication channels between IoT nodes and uses machine learning and SDN to dynamically identify and mitigate hazards. SCAFFOLD combines SDN programmability, encryption, flow data mining, and controller monitoring to offer adaptive and autonomous security for IoT installations. To strengthen security and resilience against ever-changing IoT threats, this enhanced version integrates additional concepts from related protocols like DietTOP, IKEv2, and HIP [25]. There are many security vulnerabilities that potentially compromise the privacy, availability, and integrity of IoT systems. Among these dangers include eavesdropping, data manipulation, spoofing, denial-of-service attacks, and unauthorized access. To address these many risks in a thorough manner, Enhanced SCAFFOLD utilizes a defense-in-depth strategy with several facets. Through the integration of essential security measures at many levels, the framework provides total protection. Enhanced SCAFFOLD uses encryption and session keys to provide secure channels at the communication layer, ensuring the confidentiality and integrity of data transmissions. The SDN control plane constantly examines network traffic to guarantee availability by spotting anomalies and swiftly thwarting assaults. Ensemble machine learning enables intelligent threat detection by analyzing traffic patterns and identifying potential breaches. While maintaining service for approved traffic, the SDN controller identifies suspicious flows and surgically separates them. Reauthentication and frequent key refreshes gradually reduce the effect of any compromised node. IoT systems are shielded from various threats that jeopardize their availability, confidentiality, and integrity by many levels of encryption, monitoring, intelligent detection, precise reaction, and continuous hardening. To avoid expensive public key cryptography, the nodes perform a low-cost preliminary key exchange prior to channel construction. Next, the nodes create secure channels using nonces and session keys derived from their symmetric keys. AES-256 encryption is used to secure every connection, and it is routinely rekeyed to lessen the effect of key breaches. The SDN controller passively and constantly monitors network flows using OpenFlow. Relationships between the flows, classified as assaults by a group of machine-learning classifiers, are found through correlation analysis. This enables precision mitigation, targeting specific attack sources and behaviors via SDN reconfiguration. Periodic reauthentication resets the session keys to maintain freshness. This multifaceted approach provides defense-in-depth securing for IoT systems at different layers. Encryption and authentication prevent eavesdropping and spoofing. Controller monitoring and machine learning deliver intelligent attack detection. SDN control enables surgical mitigation while maintaining availability. Periodic rekeying and reauthentication limit the impact of any node exposure. SCAFFOLD integrates essential security capabilities, including confidentiality, integrity, authorization, attack detection, and adaptive response. Our enhancements augment robustness and efficiency through standards-based cryptography, decentralized lightweight keys, ensemble learning, flow correlation analysis, and incremental session refreshment. This combination of mechanisms tailored for the IoT environments comprehensively protects system availability, reliability, and trust. We mathematically modeled the network components and defined the protocol message sequences. Detailed pseudocode precisely specifies the algorithms.