In this paper, we come up with an effective approach for the management of security using machine learning, and we derive a solution for problems with privacy and security in Internet of Things devices. Recent apps' connections to numerous IoT devices, use of edge computing, and use of fog computing cause numerous DDoS attacks to be launched against the servers of the dynamic network. For computing on the edge of the Internet of Things, the upgraded Trust Negotiation Protocol is used, making use of better period data. The application of security management is used to maintain the automation, minimize the risk level, and reduce the complexity of the system. The fundamental objective of this system is to enable user-level security in all edge computing devices related to the Internet of Things. Using Machine Learning techniques, a proposed model is utilized to develop a secure environment for E2E IoT security at the user level. A low-cost solution is obtained using machine-learning-based security management techniques. The Enhanced Trust Negotiation Protocol is simulated, and the experiment results demonstrate that the suggested model is superior to the current one in terms of the efficiency with which security management approaches may be implemented.