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2013 IEEE 5th International Conference on Cloud Computing Technology and Science (2013)
Bristol, United Kingdom United Kingdom
Dec. 2, 2013 to Dec. 5, 2013
pp: 387-394
Security is still a major concern in Cloud computing, especially the detection of nefarious use or abuse of cloud instances. One reason for this, is the ever-growing complexity and dynamic of the underlying system design and architecture. To be able to detect misuse of cloud instances, this work presents an anomaly detection system for Infrastructure as a Service Clouds. It is based on Cloud customers' usage behaviour analysis. Neural networks are used to analyse and learn the normal usage behaviour of Cloud customers, to then detect anomalies which could originate from a cloud security incident caused by an overtaken virtual machine. It increases transparency for Cloud customers about the security of their Cloud instances and supports the Cloud provider to detect misuse of their infrastructure. A simulation environment and an anomaly detection prototype get presented. Experiments validate the effectiveness of the proposed system.
Security, Training, Biological neural networks, Cloud computing, Mathematical model, Monitoring, Virtual machining,Machine Learning, Cloud Computing, Cloud Security, Neural Networks
Frank Doelitzscher, Martin Knahl, Christoph Reich, Nathan Clarke, "Anomaly Detection in IaaS Clouds", 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, vol. 01, no. , pp. 387-394, 2013, doi:10.1109/CloudCom.2013.57
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