Enhance Software-Defined Network Security with IoT for Strengthen the Encryption of Information Access Control

Area of Science:

• Computer Science
• Network Security
• Artificial Intelligence

Background:

• The Internet of Things (IoT) is rapidly expanding, necessitating robust infrastructure and security measures.
• Existing IoT networks face challenges with device credibility, packet flow stability, and energy efficiency.
• Enterprise safety initiatives require reliable communication and data access control for connected devices.

Purpose of the Study:

• To propose a novel stability routing protocol for IoT networks.
• To enhance the reliability and security of Software-Defined Network (SDN) routes within IoT environments.
• To improve data access control and energy efficiency in IoT systems.

Main Methods:

• Utilizing a stability routing protocol that assesses gadget credibility and packet flow.
• Implementing an SDN architecture for a Cognitive Protocol Network (CPN) to optimize energy levels.
• Employing Stochastic Neural Networks (SNNs) for decentralized decision-making based on perceptual packet data.
• Integrating SerIoT techniques to strengthen IoT encryption and access control.

Main Results:

• The proposed network infrastructure enhances IoT encryption and strengthens information access control.
• The system demonstrates versatility in overcoming unpredictable connectivity and node limitations.
• An end-to-end antitheft solution is generated based on statistical data and circuit restrictions.
• The study identifies critical flaws related to cluster instability, impacting platform efficiency.

Conclusions:

• The developed routing protocol and integrated techniques improve IoT security and reliability.
• SNNs and SDN contribute to decentralized decision-making and energy optimization.
• Addressing cluster instability is crucial for the overall efficiency of the IoT platform.
• The research provides a foundation for more secure and resilient IoT deployments.