Enhancing Workflow Security in Multi-Cloud Environments through Monitoring and Adaptation upon Cloud Service and Network Security Violations

TL;DR

This paper presents a novel approach for enhancing security in multi-cloud workflows by monitoring for violations and adaptively responding using learning algorithms to minimize workflow disruption.

Contribution

It introduces a comprehensive monitoring and adaptive response framework for security violations in multi-cloud workflows, employing adaptive learning to optimize reactions.

Findings

Effective detection of security violations during workflow execution.

Adaptive learning improves response selection to minimize workflow impact.

Evaluation shows improved security and workflow resilience.

Abstract

Cloud computing has emerged as a crucial solution for handling data- and compute-intensive workflows, offering scalability to address dynamic demands. However, ensuring the secure execution of workflows in the untrusted multi-cloud environment poses significant challenges, given the sensitive nature of the involved data and tasks. The lack of comprehensive approaches for detecting attacks during workflow execution, coupled with inadequate measures for reacting to security and privacy breaches has been identified in the literature. To close this gap, in this work, we propose an approach that focuses on monitoring cloud services and networks to detect security violations during workflow executions. Upon detection, our approach selects the optimal adaptation action to minimize the impact on the workflow. To mitigate the uncertain cost associated with such adaptations and their potential impact on other tasks in the workflow, we employ adaptive learning to determine the most suitable adaptation action. Our approach is evaluated based on the performance of the detection procedure and the impact of the selected adaptations on the workflows.