SVMDF: A Secure Virtual Machine Deployment Framework to Mitigate Co-Resident Threat in Cloud
Title | SVMDF: A Secure Virtual Machine Deployment Framework to Mitigate Co-Resident Threat in Cloud |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Wang, Xin, Wang, Liming, Miao, Fabiao, Yang, Jing |
Conference Name | 2019 IEEE Symposium on Computers and Communications (ISCC) |
Publisher | IEEE |
ISBN Number | 978-1-7281-2999-0 |
Keywords | AAVR, AHPTSM, allocation co-resident threat, analytic hierarchy process, analytic hierarchy process-based threat score mechanism, attack-aware VM reallocation, cloud computing, Cloud Security, CloudSim, co-resident attacks, co-resident threat defense solution, co-resident threat mitigation, co-resident-resistant VM allocation, covert channel attacks, CRRVA, load balance, migration cost, power consumption, pubcrawl, resource allocation, resource interference attacks, runtime co-resident security, secure virtual machine deployment framework, security of data, security threat, side channel attacks, SVMDF, threat score distribution, virtual machine security, virtual machines, VM allocation policy, VM migration, VM pairs, VM threat score mechanism |
Abstract | Recent studies have shown that co-resident attacks have aroused great security threat in cloud. Since hardware is shared among different tenants, malicious tenants can launch various co-resident 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 co-resident attacks. This paper combines the advantages of various countermeasures and proposes a complete co-resident threat defense solution which consists of co-resident-resistant VM allocation (CRRVA), analytic hierarchy process-based threat score mechanism (AHPTSM) and attack-aware 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 co-resident attacks, AHPTSM evaluates VM's threat score which denotes the probability that a VM is suffering or conducting co-resident 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 co-resident 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 co-resident security by migrating high threat score VMs with less migration cost. |
URL | https://ieeexplore.ieee.org/document/8969721 |
DOI | 10.1109/ISCC47284.2019.8969721 |
Citation Key | wang_svmdf_2019 |
- Cloud Security
- load balance
- CRRVA
- covert channel attacks
- co-resident-resistant VM allocation
- co-resident threat mitigation
- co-resident threat defense solution
- co-resident attacks
- CloudSim
- migration cost
- Cloud Computing
- attack-aware VM reallocation
- analytic hierarchy process-based threat score mechanism
- analytic hierarchy process
- allocation co-resident threat
- AHPTSM
- AAVR
- security threat
- VM pairs
- VM migration
- VM allocation policy
- virtual machines
- virtual machine security
- threat score distribution
- SVMDF
- side channel attacks
- VM threat score mechanism
- security of data
- secure virtual machine deployment framework
- runtime co-resident security
- resource interference attacks
- resource allocation
- pubcrawl
- power consumption