In this paper we propose a responsive autonomic and data-driven adaptive virtual networking framework (RAvN) that integrates the adaptive reconfigurable features of a popular SDN platform called open networking operating system (ONOS), the network performance statistics provided by traffic monitoring tools such as T-shark or sflow-RT and analytics and decision making skills provided from new and current machine learning techniques to detect and mitigate anomalous behavior. For this paper we focus on the development of novel detection schemes using a developed Centroid-based clustering technique and the Intragroup variance of data features within network traffic (C. Intra), with a multivariate gaussian distribution model fitted to the constant changes in the IP addresses of the network to accurately assist in the detection of low rate and high rate denial of service (DoS) attacks. We briefly discuss our ideas on the development of the decision-making and execution component using the concept of generating 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 provide the analysis on anomaly detection schemes used for detecting low rate and high rate DoS attacks versus a commonly used unsupervised machine learning technique Kmeans. The proposed schemes outperformed Kmeans significantly. The multivariate clustering method and the intragroup variance recorded 80.54% and 96.13% accuracy respectively while Kmeans recorded 72.38% accuracy.
|Title of host publication||MILCOM 2019 - 2019 IEEE Military Communications Conference|
|State||Published - Nov 2019|
|Event||2019 IEEE Military Communications Conference, MILCOM 2019 - Norfolk, United States|
Duration: Nov 12 2019 → Nov 14 2019
|Name||Proceedings - IEEE Military Communications Conference MILCOM|
|Conference||2019 IEEE Military Communications Conference, MILCOM 2019|
|Period||11/12/19 → 11/14/19|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Science Foundation under Grant Number 1738420 and by the University of Florida/Harris Corporation Excellence in Research Fellowship.
© 2019 IEEE.
- machine learning
- software-defined networks
ASJC Scopus subject areas
- Electrical and Electronic Engineering