Testbed

About: Testbed is a research topic. Over the lifetime, 10858 publications have been published within this topic receiving 147147 citations. The topic is also known as: test bed.

Flooding DDoS mitigation and traffic management with software defined networking

Abstract: Mitigating distributed denial-of-service attacks can be a complex task due to the wide range of attack types, attacker adaptation, and defender constraints. We propose a defense mechanism which is largely automated and can be implemented on current software defined networking (SDN)-enabled networks. Our mechanism combines normal traffic learning, external blacklist information, and elastic capacity invocation in order to provide effective load control, filtering and service elasticity during an attack. We implement the mechanism and analyze its performance on a physical SDN testbed using a comprehensive set of real-life normal traffic traces and synthetic attack traces. The results indicate that the mechanism is effective in maintaining roughly 50% to 80% service levels even when hit by an overwhelming attack.

Artificial Neural Network Based Automatic Modulation Classification over a Software Defined Radio Testbed

Abstract: In this paper, we design and evaluate a practical AMC system that can be readily deployed to provide robust performance in various real-time commercial scenarios. Thus, our main goal is to develop a robust AMC algorithm with low computational complexity for easy implementation and practical deployment. To this end, we utilize recently revitalized machine learning based approaches used for various classification purposes. In our proposed AMC architecture, we first propose various statistics that serve as features of the AMC signals; next, we design an artificial neural network (ANN) based classifier that performs AMC over a wide range of SNRs. We employ Nesterov accelerated adaptive moment (NADAM) estimation technique to improve the classification performance of our ANN. Further, to establish the practical feasibility of our proposed architecture, we implement it on a SDR testbed. The proposed ANN-based classifier is shown to outperforms the hybrid hierarchical AMC (HH-AMC) system and is flexible enough to easily expand the dictionary of modulation formats for other applications.

An experimental framework for the evaluation of cooperative diversity

Abstract: Cooperative diversity is the result of relaying among nodes to achieve space diversity in multipath environments that offer limited time and frequency diversity. Although there is now substantial literature covering specification and analysis of cooperative communication strategies based upon models of wireless environments, there is much less work addressing experiments with real-world radio hardware and propagation channels. This work describes the construction of a three node, experimental testbed based upon a network of software-defined radios for development and verification of cooperative protocols. Several decode-and-forward relay protocols have been implemented and evaluated in terms of their diversity gains as measured from experimental curves of bit-error rate versus average signal-to-noise ratio. In contrast to the few other implementation efforts reported, the experimental setup maintains the relative node geometry while moving the network to induce fading, and the experimental results exhibit diversity benefits.

The virtual node layer: a programming abstraction for wireless sensor networks

Abstract: The Virtual Node Layer (VNLayer) programming abstraction provides programmable, predictable automata--virtual nodes--emulated by the low-level network nodes This simplifies the design and rigorous analysis of applications for the wireless sensor network setting, as the layer can mask much of the uncertainty of the underlying components In this paper, we define a general VNLayer architecture, and then use this framework to design a practical VNLayer implementation, optimized for real-world use We then discuss our experience deploying this implementation on a testbed of hand-held computers, and in a custom-built packet-level simulator, and present a sample application--a virtual traffic light--to highlight the power and utility of our abstraction We conclude with a survey of additional applications that are well-suited to this setting

Scalable Traffic Sampling Using Centrality Measure on Software-Defined Networks

Abstract: With regard to cyber security, pervasive traffic visibility is one of the most essential functionalities for complex network systems. A traditional network system has limited access to core and edge switches on the network; on the other hand, SDN technology can provide flexible and programmable network management operations. In this article, we consider the practical problem concerning how to achieve scalable traffic measurement using SDN functionalities. Less intrusive traffic monitoring can be achieved by using a packet sampling technique that probabilistically captures data packets at switches, and the sampled traffic is steered toward a traffic analyzer such as an IDS on SDN. We propose the use of a centrality measure in graph theory for deciding the traffic sampling points among the switches. In addition, we discuss how to decide the traffic sampling rates at the selected switches. The results of the simulation and SDN testbed experiments indicate that the proposed sampling point and rate decision methods enhance the intrusion detection performance of an IDS in terms of malicious traffic flows in large-scale networks.