# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 7 Conclusions and Future Work  
This paper presented the Enhanced SCAFFOLD protocol, a comprehensive security framework tailored for IoT environments. Enhanced SCAFFOLD establishes resilient encrypted channels between IoT nodes using session keys derived from preliminary lightweight key exchanges. The integration of SDN enables continuous monitoring of network flows and traffic patterns. Ensemble machine-learning classifiers reliably detect anomalies that indicate potential attacks. The SDN control plane allows precision mitigation by surgically blocking only suspicious flows while maintaining availability. Periodic reauthentication and rekeying provide freshness against compromise over time. Detailed mathematical modeling concisely captures Enhanced SCAFFOLD's mechanisms and logic flows. Precise protocol syntax and structured pseudocode algorithms define the sequence of operations and data exchanges. Extensive simulations quantified Enhanced SCAFFOLD's ability to detect and rapidly mitigate various attack types, including distributed denial of service, reconnaissance probes, man-in-the-middle attacks, and malware infections. The results validated the low latency overhead of the protocol for maintaining quality of service during threats. The energy consumption analysis highlighted efficiency tradeoffs for more constrained IoT devices. This multidimensional defense-in-depth approach integrates essential security capabilities like confidentiality, integrity, authorization, intelligent threat detection, and adaptive response to protect modern IoT systems. While the Enhanced SCAFFOLD framework presents a promising approach for securing IoT environments, there are several limitations and challenges that need to be addressed for its practical implementation. Future research should focus on enhancing the scalability of the framework to handle the demands of large-scale IoT deployments. Investigating distributed SDN architectures, efficient load balancing mechanisms, and techniques such as hierarchical control plane design and edge computing could help distribute the processing load and improve its responsiveness. Future development must also focus on ensuring interoperability with the wide variety of IoT devices and protocols. Protocol adapters, standardized interfaces, and collaborations with IoT device manufacturers and standards organizations enable widespread adoption and smooth integration of the Enhanced SCAFFOLD architecture. Continuous security testing and analysis are needed to detect and remedy framework vulnerabilities. Code audits, penetration testing, and framework component verification should be a part of future studies. Participating in bug bounty programs and security research may help uncover and fix problems quickly. Further study should include machine learning simulation into the Enhanced SCAFFOLD performance assessment. The ensemble classifier and other ML components must be tested in a number of IoT settings to ensure its real-world applicability and performance. This is essential for building and using the Enhanced SCAFFOLD structure. Privacy-preserving solutions for the Enhanced SCAFFOLD architecture are another interesting research subject. Homomorphic encryption, secure multi-party computing, and differential privacy protect data aggregation and analysis. To achieve acceptance, the Enhanced SCAFFOLD framework must be more appealing and user-friendly. Provide clear instructions, setup tools, and interfaces. Usability concerns may be identified and fixed using user surveys and deployment feedback. This research received no external funding. Not applicable. *Research data will be available on individual requests to the corresponding author considering collaboration possibilities with the researcher or research team and with restrictions that the data will be used only for further research in the related literature progress. The author would like to thank the Deanship of Scientific Research at Arab American University for supporting this research. The author declares no conflict of interest.