TY - GEN
T1 - Enhancing Workflow Security in Multi-cloud Environments Through Monitoring and Adaptation upon Cloud Service and Network Security Violations
AU - Soveizi, Nafiseh
AU - Karastoyanova, Dimka
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/10/25
Y1 - 2023/10/25
N2 - 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.
AB - 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.
KW - Adaptation Recommendation
KW - Cloud Service Monitoring
KW - Cloud-based workflows
KW - Security-aware workflows
KW - Violation detection
KW - Workflow Adaptation
UR - http://www.scopus.com/inward/record.url?scp=85175957661&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-46846-9_9
DO - 10.1007/978-3-031-46846-9_9
M3 - Conference contribution
AN - SCOPUS:85175957661
SN - 9783031468452
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 175
BT - Cooperative Information Systems - 29th International Conference, CoopIS 2023, Proceedings
A2 - Sellami, Mohamed
A2 - Gaaloul, Walid
A2 - Vidal, Maria-Esther
A2 - van Dongen, Boudewijn
A2 - Panetto, Hervé
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Cooperative Information Systems, CoopIS 2023
Y2 - 30 October 2023 through 3 November 2023
ER -