| 127 | | This demo shows a collaborative monitoring and correlation approach to mitigate the effects of the surge in network traffic of a flooding Denial of Service attack that can cause loss of service for legitimate sites. |
| 128 | | |
| 129 | | Who should see this demo? |
| 130 | | |
| 131 | | Attendees interested in Cybersecurity attack detection, and mitigation techniques. |
| 132 | | |
| 133 | | Software-defined networking (SDN) and OpenFlow offer great support to dynamically adapt a network and to access data on different network layers as needed. Such advantages have been driving recent research efforts to develop new security applications and services. However, most studies on attack detection and containment have not really differentiated their solutions from the traditional ones, without fully taking advantage of the unique capabilities provided by SDN. Moreover, even if some of these studies provide interesting visions of what can be achieved, they stop short of presenting realistic application scenarios and experimental results. We present a novel attack detection and containment approach that is coordinated by distributed network monitors and controllers/correlators centralized on an SDN OpenFlow Virtual Switch (OVS). With different views and information availability, these elements collaboratively detect signature constituents of an attack that possess different characteristics of scale and detail. Therefore, this approach is able to not only quickly issue an alert against potential threats followed by careful verification for high accuracy, but also balance the workload on the OVS. We apply the proposed approach to TCP SYN flood attacks using Global Environment for Network Innovations (GENI). This realistic experimentation has provided us with insightful findings helpful to our goal toward a systematic methodology of SDN-supported attack detection and containment. First, we have demonstrated through experimentation the scalability of our collaborative scheme. Second, we have studied how the combination of alerts by the monitor and deep packet inspection by the correlator, can increase the speed and accuracy of attack identification. Our experiments, in the context of a small to medium corporate network, have demonstrated the effectiveness and scalability of the SDN-supported detection and containment approach. |
| | 127 | ''This demo uses collaborative monitoring and correlation to mitigate effects of the network traffic surge of a flooding Denial of Service attack that can cause loss of service for legitimate sites. Visit us to learn more about cybersecurity attack detection and mitigation.'' |
| | 128 | |
| | 129 | Software-defined networking (SDN) and OpenFlow offer great support to dynamically adapt a network and to access data on different network layers as needed. Such advantages have been driving recent research efforts to develop new security applications and services. However, most studies on attack detection and containment have not really differentiated their solutions from the traditional ones, without fully taking advantage of the unique capabilities provided by SDN. Moreover, even if some of these studies provide interesting visions of what can be achieved, they stop short of presenting realistic application scenarios and experimental results. We present a novel attack detection and containment approach that is coordinated by distributed network monitors and controllers/correlators centralized on an SDN OpenFlow Virtual Switch (OVS). With different views and information availability, these elements collaboratively detect signature constituents of an attack that possess different characteristics of scale and detail. Therefore, this approach is able to not only quickly issue an alert against potential threats followed by careful verification for high accuracy, but also balance the workload on the OVS. We apply the proposed approach to TCP SYN flood attacks using the Global Environment for Network Innovations (GENI). This realistic experimentation has provided us with insightful findings helpful to our goal toward a systematic methodology of SDN-supported attack detection and containment. First, we have demonstrated through experimentation the scalability of our collaborative scheme. Second, we have studied how the combination of alerts by the monitor and deep packet inspection by the correlator, can increase the speed and accuracy of attack identification. Our experiments, in the context of a small to medium corporate network, have demonstrated the effectiveness and scalability of the SDN-supported detection and containment approach. |
| 146 | 145 | |
| 147 | 146 | Hadoop is a !MapReduce implementation for processing and generating large data sets. Combined with the ubiquitous, on-demand, and dynamic resources at low cost from cloud computing, we can build an environment with great potential to process big data. However, using Hadoop on the cloud spends time, requires technical knowledge from users, and, sometimes, the private cloud is not able to allocate all the resources needed. The hybrid cloud is composed of public and private clouds and, when necessary, the resources in the public cloud are used. Therefore, the simultaneous management of private and public domains requires an appropriate model that combines performance with minimal cost. Our proposition is to deploy an architecture to facilitate the orchestration of Hadoop applications in hybrid clouds. The core of the model consists of a submission web portal, an orchestration engine, and an execution services factory. These components will orchestrate the creation of virtual machines for the Hadoop clusters in the private cloud. Through these components it is possible to automate the preparation of a cross-domain cluster, and, when it is needed, to allocate virtual machines at the GENI platform, and make it useful for the cloud users. |
