MFedL-MTD: Enhancing Cloud Security via Multi level Federated Learning-based Optimal Task Offloading
Keywords:
Cloud Security, Task Offloading, Multi-level Federated Learning, Multi-factor Authentication, Federated Learning Secure Aggregation.Abstract
In the realm of cloud security, optimal task offloading entails the strategic assignment of computational tasks to cloud resources, bolstering efficiency, scalability, and resilience in the face of potential security vulnerabilities.During task offloading in cloud computing, security concerns encompass data privacy, the potential for sensitive information leaks, and the risks posed by unauthorized access to cloud resources. Henceforth, optimal task offloading is essential to enhance cloud security as it optimizes resource utilization, reduces latency, and minimizes attack surfaces, thus mitigating potential security risks. Recent works are majorly focused on task offloading however, researches are limited user privacy and handling of cloud server overflowing issues. To overcome these issues, we introduced MFedL-MTD framework which encompasses Multi-level Federated Learning with Moving Target Defense (MTD) technique for enhancing task offloading security. In this work, cloud user’s authenticity is ensured by performing Multi-factor Authentication (MFA) thereby authenticating legitimate users via Trust Authority (TA). Following that, Multi-level Federated Learning is adapted for executing effecting user selection in edge server. Here, local model, global model and super model are generated and aggregated by utilizing Federated Learning Secure Aggregation (FL-SA) which improves accumulation performance. Moreover, task classification is implemented using N-beats and then task scheduling is accomplished through considering appropriate features. At last, MTD is performed for VM placement and VM migration when cloud server is overflows thus tends to effective and secure task offloading. The proposed MFedL-MTD framework can be evaluated using various metrics as latency, computation complexity, security strengthen, migration time and energy consumption alongside comparisons with state-of the-art approaches, demonstrating its superior performance over existing models.
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