Citation :

Naren Raghavendra Suri, S.Gowtham Bharath, "Abnormal Behavior Detection using Machine Learning In a Virtual Mobile Cloud Infrastructure," International Journal of Computer Trends and Technology (IJCTT), vol. 3, no. 6, pp. 33-35, 2013. Crossref, https://doi.org/10.14445/22492615/IJPTT-V3I10P104

Abstract

— At present many mobile services are converting to cloud depended mobile services with high communications and greater flexibility. We explore a unique mobile cloud infrastructure that attaches mobiles and cloud services. This fresh infrastructure gives mobile instances, which are virtual among cloud computing. In order to enter into marketing with such infrastructure, the service providers should know about the security openings. Hence, in this paper, we initially detailed different mobile cloud services extending into mobile cloud infrastructure, and explained various service scenarios to unveil the possible security threats. Then, we detailed the architecture and methodology for abnormal behavior detection through the observation of host and network data. To check our methodology, we inserted malicious programs into our mobile cloud test bed and utilized a machine learning algorithm-Random Forest- to find out abnormal behavior’s from those.

Keywords

- Decentralized erasure code, secure storage system, abnormal detection, random forest, decision tree.

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