Adversarial Machine Learning-Based Algorithms for Mobile Network Security Event Traceability and Responsibility Tracking

Conventional mobile network security event source tracing and responsibility tracking algorithms mainly use the DDoS (Distributed Denial of Service) distributed denial model to track attack sources, which are susceptible to the sensitivity of traffic changes, resulting in poor overall performance. Therefore, a method is proposed for Mobile network security event traceability and responsibility tracking algorithm based on adversarial machine learning, that is, using adversarial machine learning to build a mobile network security event traceability and responsibility tracking model, and designing network security event traceability and tracking processes, thereby realizing mobile network security event traceability and accountability tracking. Experimental results show that the designed adversarial machine learning traceability and responsibility tracking algorithm for mobile network security events has good comprehensive performance indicators and has a certain application value. It has made certain contributions to reducing mobile network security risks and improving mobile network transmission quality.