Abstract
Virtualized infrastructure in cloud computing is becoming popular day-by-day due to its advanced facilities like storing massive data. On the other hand, the attempt to attack these computer networks is growing as it contains qualitative information of several enterprises. To overcome these challenges, a big data-based autonomous anomaly detection using clustering and classification approaches is proposed in this paper. The main aim is to identify the presence of attacks and classify their types in the virtualized cloud infrastructure. The big data are initially stored in a machine called Hadoop Distributed File System. The data are captured and clustered for ease of analysis. The classification task of security analytics is performed with machine learning processes, namely, supervised learning. Moreover, the accuracy of attack classification is verified through Receiver Operator Characteristics Curve. This method is proved to be effective for attack detection in big data storage cloud systems.
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Diaz, P.M., Julie Emerald Jiju, M. (2022). Big Data-Based Autonomous Anomaly Detection Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing. In: Agrawal, R., He, J., Shubhakar Pilli, E., Kumar, S. (eds) Cyber Security in Intelligent Computing and Communications. Studies in Computational Intelligence, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-16-8012-0_6
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