Deep-Learning-as-a-Service for Digital Twins: Ensuring Security with Adaptive QOS Techniques Using Secureflex Algorithm

The increasing application of Digital Twins in important sectors, including industry, medical practice, and defense, calls for reliable and elastic learning as a service (DLaaS). However, these systems are confronted with many issues in ensuring data privacy and assurance of a stable Quality of Service (QoS) under fluctuating workloads and Malicious User (MU) attacks. To this end, SecureFlex is proposed in this paper as a security enhancement algorithm for DLaaS systems in DT applications. SecureFlex is a system that uses multiple levels of threat intelligence, which include real-time threat detection, resource management, and QoS adjustments. The algorithm utilizes encryption to enhance data confidentiality and machine learning reliability while controlling computational resources in response to QoS changes. Several experiments were performed on Digital Twin across predictive maintenance and real-time anomaly detection usage cases. This performance evaluation shows that the introduced method, SecureFlex, enhances system security against cyber-attacks and optimizes QoS features like the capacity for latency, throughputs, and response accuracy to an extent of 25 % in comparison to approaches in the existing literature. This research shows how SecureFlex could help solve security and efficiency issues in DLaaS systems, preparing for higher reliability and effectiveness of Digital Twin in critical deployments.