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Cloud computing has emerged as a crucial solution for handling data- andcompute-intensive workflows, offering scalability to address dynamic demands.However, ensuring the secure execution of workflows in the untrustedmulti-cloud environment poses significant challenges, given the sensitivenature of the involved data and tasks. The lack of comprehensive approaches fordetecting attacks during workflow execution, coupled with inadequate measuresfor reacting to security and privacy breaches has been identified in theliterature. To close this gap, in this work, we propose an approach thatfocuses on monitoring cloud services and networks to detect security violationsduring workflow executions. Upon detection, our approach selects the optimaladaptation action to minimize the impact on the workflow. To mitigate theuncertain cost associated with such adaptations and their potential impact onother tasks in the workflow, we employ adaptive learning to determine the mostsuitable adaptation action. Our approach is evaluated based on the performanceof the detection procedure and the impact of the selected adaptations on theworkflows.

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