| EPSRC Reference: |
EP/J020478/1 |
| Title: |
Rogue Virtual Machine Identification in DaISy Clouds |
| Principal Investigator: |
Guo, Professor Y |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computing |
| Organisation: |
Imperial College London |
| Scheme: |
Standard Research |
| Starts: |
15 May 2012 |
Ends: |
14 May 2013 |
Value (£): |
119,691
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
Information Technologies |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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09 Feb 2012
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Data Intensive Systems (DaISy)
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Announced
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Summary on Grant Application Form |
Our work in this proposal focuses primarily on the safe and secure cloud computing challenge of the EPSRC DaISy call. In addition, it also addresses closely issues relevant to the extracting meaningful information challenge, specifically extracting meaning from large-scale monitoring information collected in a cloud computing environment, and the ensuring confidence in collaborative working challenge by developing meta-data and methods that enable users to monitor how their digital assets are being used in shared environments.
The proposal builds on the investigators' expertise in building secure cloud computing systems (especially the IC-Cloud system), developing large-scale data analysis systems and developing algorithms and methods for the security analysis of program behaviour to develop develop and evaluate novel methods to detect subtle attacks by adversaries who have already gained access to a VM within a secure cloud system.
Our general methodology for this 1-year project is based on 1) Building on the existing IC-Cloud platform as a test-bed our research. The use of the in-house infrastructure based on the popular XEN Hypervisor enables us to rapidly develop and evaluate monitoring tools, to develop and test different attack scenarios and collect the log data from the real applications. 2) Building on our in-house repertoire of data mining and analysis tools developed over the years for classification, clustering and association analysis and on our recent expertise developed in real-time data mining methods and Bayesian analysis frameworks for modeling and analysis anomalies in large scale sensor data for environmental and security applications. 3) Close collaboration with partners and collaborators of the Institute and Security Science and Technology to receive ongoing feedback on our methodology, results and methods as we develop them.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.imperial.ac.uk |