SVMDF: A Secure Virtual Machine Deployment Framework to Mitigate CoResident Threat in Cloud Recent studies have shown that coresident attacks have aroused great security threat in cloud. Since hardware is shared among different tenants, malicious tenants can launch various coresident attacks, such as side channel attacks, covert channel attacks and resource interference attacks. Existing countermeasures have their limitations and can not provide comprehensive defense against coresident attacks. This paper combines the advantages of various countermeasures and proposes a complete coresident threat defense solution which consists of coresidentresistant VM allocation (CRRVA), analytic hierarchy processbased threat score mechanism (AHPTSM) and attackaware VM reallocation (AAVR). CRRVA securely allocates VMs and also takes load balance and power consumption into consideration to make the allocation policy more practical. According to the intrinsic characteristics of coresident attacks, AHPTSM evaluates VMxe2x80x99s threat score which denotes the probability that a VM is suffering or conducting coresident attacks based on analytic hierarchy process. And AAVR further migrates VMs with extremely high threat scores and separates VM pairs which are likely to be malicious to each other. Extensive experiments in CloudSim have shown that CRRVA can greatly reduce the allocation coresident threat as well as balancing the load for both CSPs and tenants with little impact on power consumption. In addition, guided by threat score distribution, AAVR can effectively guarantee runtime coresident security by migrating high threat score VMs with less migration cost.

Bayesian attack graphs for platform virtualized infrastructures in clouds Abstract   Virtualization security is an important aspect to be carefully addressed while provisioning cloud services. In this paper, we propose a novel model using Bayesian Attack Graphs (BAG) to perform security risk assessment for platform virtualized infrastructures that are used for building cloud services. BAGs are powerful mechanisms that can be used to model the uncertainties inherent in security attacks. We build upon the reference conditional probability tables for the BAG nodes using the reported attacks on virtualized systems from the Common Vulnerabilities and Exposures (CVE) database. We employ Bayesian probabilistic inference techniques on the model presented and showcase the results obtained that can be used by system architects for the risk assessment of such infrastructures. In addition to the probabilistic model, we also present a deterministic approach with security metrics for attack graphs and derive the values for the modeled BAG, which can be used for assessing and comparing with other architectures. The approach described here to draw inferences from the BAG can be employed by system architects to find explanations to critical queries in security design and also to carefully select the countermeasures to be installed. The model can also be used to learn from future aposteriori evidence data from actual security breaches to provide an efficient risk assessment.