Biblio

Modern infrastructure is heavily reliant on systems with interconnected computational and physical resources, named Cyber-Physical Systems (CPSs). Hence, building resilient CPSs is a prime need and continuous monitoring of the CPS operational health is essential for improving resilience. This paper presents a framework for calculating and monitoring of health in CPSs using data driven techniques. The main advantages of this data driven methodology is that the ability of leveraging heterogeneous data streams that are available from the CPSs and the ability of performing the monitoring with minimal a priori domain knowledge. The main objective of the framework is to warn the operators of any degradation in cyber, physical or overall health of the CPS. The framework consists of four components: 1) Data acquisition and feature extraction, 2) state identification and real time state estimation, 3) cyber-physical health calculation and 4) operator warning generation. Further, this paper presents an initial implementation of the first three phases of the framework on a CPS testbed involving a Microgrid simulation and a cyber-network which connects the grid with its controller. The feature extraction method and the use of unsupervised learning algorithms are discussed. Experimental results are presented for the first two phases and the results showed that the data reflected different operating states and visualization techniques can be used to extract the relationships in data features.

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.