In this demonstration, we present new capabilities of Intelligent Automation Inc. (IAI) Radio Frequency Network Emulation Simulation Tool (RFnestTM) (www.i-a-i.com/rfnest). RFnestTM is a Field Programmable Gate Array (FPGA) based network channel emulator that allows all of the channels of a full network of radio nodes to be emulated in real time, with all communication nodes experiencing a realistic channel impulse response. This allows RFnestTM to be used for protocol testing, replaying field tests, and model validation. RFnestTM has a modular design with three main capabilities: 1) FPGA based emulation hardware with RF front ends that allows nodes with real radios to send their RF signal over an emulated channel without any modification to the radio, 2) modeling of time-varying channel impulse responses within the emulation hardware, with channel properties based on mobility defined with a scripted or interactive Graphic User Interface (GUI) environment, and 3) integration with network emulators and monitoring functionality that allows the user to instantiate, manage, and monitor real and virtual network nodes within the scenario. In the new version of RFnestTM we now support: Frequency programmability: The new RFnestTM allows users to change the center frequency through software from 800 Mhz to 2.7GHz. Increased bandwidth: RFnestTM supports a bandwidth of 60 MHz. Higher fidelity channel modeling: Through software and hardware updates RFnestTM support wireless channels with up to 20 taps and a wide range of Doppler frequencies. MIMO: RFnestTM now supports different combination of MIMO channel between transmitters and receivers.

Capturing the uncertain aspects in cyber security is important for security analysis in enterprise networks. However, there has been insufficient effort in studying what modeling approaches correctly capture such uncertainty, and how to construct the models to make them useful in practice. In this paper, we present our work on justifying uncertainty modeling for cyber security, and initial evidence indicating that it is a useful approach. Our work is centered around near real-time security analysis such as intrusion response. We need to know what is really happening, the scope and severity level, possible consequences, and potential countermeasures. We report our current efforts on identifying the important types of uncertainty and on using Bayesian networks to capture them for enhanced security analysis. We build an example Bayesian network based on a current security graph model, justify our modeling approach through attack semantics and experimental study, and show that the resulting Bayesian network is not sensitive to parameter perturbation.

Prior measurement and analysis have shown that the traffic in data networks is better described by heavy-tailed (HT) distributions. For ad hoc routing, it is important to understand the impact of the heavy-tailed nature of network traffic for routing decisions since heavy-tailed workload can lead to severe performance degradation. In this paper, we present our current work on ad hoc routing by exploiting the heavy-tailed nature of the session (flow) lifetime. In particular, long- and shortlived flows are differentiated based on the unique predictability nascent in heavy-tailed distributions, with the long-lived flows distributed across the network as disperse as possible, and the short-lived flows treated as "filler" traffic. We have implemented such ideas in various ad hoc routing protocols, and in this paper we present our results experimenting on top of dynamic source routing (DSR). Simulation results reveal that our modified protocol, called DSR-HT, significantly outperforms the state-of- the-art under heavy-tailed workload.

Extensive measurement and analysis have shown that traffic in data networks is better described by heavy-tailed distributions. We present our current work on ad hoc routing by exploiting the heavy-tailed nature of the flow lifetime. In particular, the long- and short-lived flows are differentiated based on the unique predictability nascent in heavy- tailed distributions, with the long- and short-lived flows being handled separately. A modified AODV has been implemented to enable heavy-tailedness awareness. Simulation results reveal that the new protocol, AODV-HT, significantly outperforms the state-of-the-art under heavy-tailed workload.