4.7 Article

Co-Residence Data Theft Attacks on N-Version Programming-Based Cloud Services With Task Cancelation

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2020.3002930

关键词

Cloud computing; data security; N-version programming (NVP); threshold voting; virtual machine (VM)

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This article models the co-residence data theft attacks in a cloud service and proposes a threshold voting-based N-version programming solution to improve service reliability and reduce expenses. Probabilistic models are introduced to evaluate performance metrics and optimization problems are solved to find the optimal number of service component versions. The interactions among parameters and policies and their effects on performance metrics and optimization solutions are studied through examples.
Powered by virtualization, the cloud computing has brought good merits of cost effective and on-demand resource sharing among many users. On the other hand, cloud users face security risks from co-residence attacks when using this virtualized platform. Particularly, a malicious attacker may create side channels to steal data from a target user's virtual machine (VM) that co-resides with the attacker's VM on the same physical server. This article models a cloud service undergoing the co-residence data theft attacks. The thresholdvoting-based N-version programming (NVP) is implemented to improve the service reliability, where multiple service component versions (SCVs) are activated in parallel to perform the requested service. The final output is determined upon receiving a threshold number of identical outputs from the SCVs, immediately followed by canceling all outstanding SCVs to reduce expenses. Probabilistic models are first introduced to evaluate performance metrics of the considered service, including the data theft probability, service success probability, expected service operation time, and expected utility. Optimization problems are further solved to find the optimal number of SCVs maximizing the expected utility. Interactions among different model parameters and VM allocation policies, as well as their effects on the considered performance metrics and on the optimization solutions are studied through examples.

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