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In this paper, we propose a responsive autonomic and data-driven adaptive virtual networking framework (RAvN) to detect and mitigate anomalous network behavior. The proposed detection scheme detects both low rate and high rate denial of service (DoS) attacks using (1) a new Centroid-based clustering technique, (2) a proposed Intragroup variance technique for data features within network traffic (C.Intra) and (3) a multivariate Gaussian distribution model fitted to the constant changes in the IP addresses of the network. RAvN integrates the adaptive reconfigurable features of a popular SDN platform (open networking operating system (ONOS)); the network performance statistics provided by traffic monitoring tools (such as T-shark or sflow-RT); and the analytics and decision-making tools provided by new and current machine learning techniques. The decision making and execution components generate adaptive policy updates (i.e. anomalous mitigation solutions) on-the-fly to the ONOS SDN controller for updating network configurations and flows. In addition, we compare our anomaly detection schemes for detecting low rate and high rate DoS attacks versus a commonly used unsupervised machine learning technique, Kmeans. Kmeans recorded 72.38% accuracy, while the multivariate clustering and the Intra-group variance methods recorded 80.54% and 96.13% accuracy respectively, a significant performance improvement.