Cloud Computing Security aspects: Threats, Countermeasures and Intrusion Detection using Support Vector Machine

Abstract

The Cloud Computing concept has witnessed immense growth, and the rationale behind this increased interest in the emerging paradigm is the cost-effective transmission, storage and intensive computation.  The idea is to render remote storage and data analysis capability to the end-user using shared computing resources, thereby bringing down the total cost incurred for an individual. However, consumers are still reluctant to adopt this technology due to associated security and privacy concerns. This manuscript presents a comprehensive description of various threats and technical security concerns with respect to cloud computing. Additionally, support vector machine was used to detect intrusion on the NSL-KDD dataset designed by DARPA. The accuracy of the algorithm for DoS and probe attack were analyzed, and the results are represented in the form of confusion matrices. Among the 41 attributes of the NSL-KDD dataset, only 24 significant attributes are selected. We observed that our model could predict a DoS attack with an accuracy of 96% and a probe attack with an accuracy of 84%.