DocumentCode :
3435298
Title :
Anomaly Detection in IaaS Clouds
Author :
Doelitzscher, Frank ; Knahl, Martin ; Reich, Christoph ; Clarke, N.
Author_Institution :
Cloud Res. Lab., Furtwangen Univ., Furtwangen, Germany
Volume :
1
fYear :
2013
fDate :
2-5 Dec. 2013
Firstpage :
387
Lastpage :
394
Abstract :
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.
Keywords :
cloud computing; neural nets; security of data; IaaS clouds; anomaly detection; cloud computing; cloud customer usage behaviour analysis; cloud provider; cloud security incident; infrastructure as a service; nefarious use detection; neural networks; overtaken virtual machine; system architecture; system design; Biological neural networks; Cloud computing; Mathematical model; Monitoring; Security; Training; Virtual machining; Cloud Computing; Cloud Security; Machine Learning; Neural Networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on
Conference_Location :
Bristol
Type :
conf
DOI :
10.1109/CloudCom.2013.57
Filename :
6753822
Link To Document :
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