Enhancing Security in 5G Networks: A Hybrid Machine Learning Approach for Attack Classification

Over the last decade, the demand for greater security in 5G networks has grown significantly. Ensuring data security during transmission against external attacks has become a critical priority. However, existing security systems, which focus on attack identification, face limitations in terms of both security and performance. This requires the implementation of more rigorous measures. Meeting the need for improved security in 5G networks calls for advanced machine learning techniques. To tackle this challenge, a proposed hybrid mechanism employs various machine learning approaches to effectively classify threats, such as denial of service, detection denial, and resource misuse. The incorporation of the DET model improves the accuracy of decision making and improves attack classification for 5G networks. Key accuracy parameters, including recall, precision, and F-score, play a crucial role in ensuring the model’s reliability. Simulation results demonstrate the superiority of the proposed model compared to others, particularly in terms of accuracy. Our approach presents a promising solution for identifying and categorizing attacks in 5G networks. By prioritizing accuracy and providing superior performance, this research significantly contributes to ongoing efforts to improve 5G network security.