Online anomaly detection using non-parametric technique for big data streams in cloud collaborative environment
Smrithy Girijakumari Sreekantan Nair, Sathyan Munirathinam, Ramadoss Balakrishnan · 2016
Big Data and cloud computing are complementary technological paradigms with a core focus on scalability, agility, and on-demand availability. The rise of cloud computing and cloud data stores have been a precursor and facilitator to the emergence of big data. Cloud computing turns traditional siloed computing assets into shared pools of resources that are based on an underlying internet foundation. As a result a number of enterprises are building efficient and agile cloud environments, and cloud providers continue to expand service offerings. Many cloud providers offer online collaboration service which is basically loosely-coupled in nature. Online anomaly detection aims to detect anomalies in data flowing in a streaming fashion. Such stream data is commonplace in today's cloud centric collaborations which enables participating domains to dynamically interoperate through sharing and accessing of information. Accordingly to forestall unauthorized disclosure of the shared resources and conceivable misappropriation, there is a need to identify anomalous access requests. To the best of our knowledge, the detection of anomalous access requests in cloud-based collaborations through non-parametric statistical technique has not been studied in earlier works. This paper proposes an online anomaly detection algorithm based on non-parametric statistical technique to detect anomalous access requests in cloud environment at runtime.