References:

Y. Zhang, A. Baliga, W. Trappe, "Reactive On-board Regulation of Cognitive Radios", Proceedings of the First International Workshop on Data Security and PrivAcy in Wireless Networks (D-SPAN 2010).

R. Dudheria, W. Trappe and N. Minsky, “Coordination and Control in Mobile Ubiquitous Computing Applications Using Law Governed Interaction,” in Proceedings of UBICOMM 2010.

S. Liu, L. Greenstein, Y. Chen, W. Trappe, “ALDO: An Anomaly Detection Framework for Dynamic Spectrum Access Networks," in Proceedings of the 28th IEEE International Conference on Computer Communications,

April 2009.

 

 

 

AUSTIN: An Initiative to Assure Software Radios have Trusted Interactions

Project Objectives:
Software radios represent a platform that could easily be reprogrammed to act in an unregulated manner. The objective behind the AUSTIN project is to develop a suite of security solutions that can regulate software radios that may be maliciously programmed to operate in an unregulated and inappropriate manner.

 

Technology Rationale:
Software and cognitive radios will greatly improve the capabilities of wireless devices to adapt their protocols and improve communication. Unfortunately, the benefits that such technology will bring are coupled with the ability to easily reprogram the protocol stack. Thus it is possible to bypass protections that have generally been locked within firmware. If security mechanisms are not developed to prevent the abuse of software radios, adversaries may exploit these programmable radios at the expense of the greater good.

Technical Approach:
Regulating software radios requires a holistic approach, as addressing threats separately will be ineffective against adversaries that can acquire, and reprogram these devices. The AUSTIN project involves a multidisciplinary team from the Wireless Information Network Laboratory (WINLAB) at Rutgers University, the Wireless@Virginia Tech University group, and the University of Massachusetts. AUSTIN will identify the threats facing software radios, and will address these threats across the various interacting elements related to cognitive radio networks. Specifically, AUSTIN is examining: (1) the theoretical underpinnings related to distributed system regulation for software radios; (2) the development of an architecture that includes trusted components and a security management plane for enhanced regulation; (3) onboard defense mechanisms that involve hardware and software-based security; and (4) algorithms that conduct policy regulation, anomaly detection/punishment, and secure accounting of resources.

 

 

Results To Date and Future Work Plan:

We have formulated the problem of detecting anomalous activity in a dynamic spectrum access scenario, where licensed and unlicensed transmitters are deployed in an area with an auxiliary sensor network being deployed as infrastructure to detect non-authorized usage of a spectral band by a secondary transmitter. The underlying spectrum policy that was studied can be summarized as 'User U is allowed to use frequency band k from time T1 to T2 as long as its power levels do not exceed L dBm in a region A'. To detect that the user complies with this policy, the auxiliary sensor grid monitors spectrum levels. The detection of anomalous usage of spectrum by user U is then formulated as a statistical significance testing problem, and was formulated for two cases: an unauthorized transmission being present in background noise, and an unauthorized transmission being present when authorized signals are present. We found that it was necessary to examine different cooperative decision strategies, whereby information from spatially distinct sensors is combined to conclude a decision. For the case where no authorized signal was present, we found that hard decision combining outperformed soft decision combining in scenarios where the environment experiences significant path loss (fading), yet soft decision combining is near optimal in environments where the effect of fading is minor (e.g. may be ignored).

On the problem of identifying cognitive radio nodes and the presence of wireless networks, the team has been able to show that although it is possible to load different software modules to identify each potential service, such an approach is needlessly inefficient. Instead, rather than use a collection of complete protocols on a cognitive radio, it was shown that it is essential to have a separate identification module that is capable of reliably identifying services and devices while minimizing the code needed. In particular, by effectively leveraging protocol-specific properties, it is possible to utilize data from narrowband spectral sampling in order to identify broader band services and individual devices. The utility of such efficient service and device detection was verified using the GNU Radio and the Universal Software Radio Peripheral (USRP) platform by identifying radio services in the industrial, scientific, and medical (ISM) radio band. Further, it was shown that physical layer signatures may be used to reliably identify devices, thereby allowing CRs to exploit physical layer information in support of basic authentication functionality.

Lastly, on the problem of developing a secure cognitive radio medium access control protocol, the team has implemented a software module, called the onboard regulator module (ORM) that can run on a USRP in parallel with user-space MAC protocols. The ORM monitors transmissions coming from the cognitive radio and, by collecting statistics related to other radio acknowledgements, the ORM can assess whether the cognitive radio's activities adversely affect the operation of other radios. If it is deemed that a cognitive radio's transmissions would hurt the quality of service experienced by other radios, then the ORM punishes its cognitive radio by randomly dropping packets from the transmission queue.

Contact:
Prof. Wade Trappe
732-932-6857 Ext. 64
4
trappe (AT) winlab (DOT) rutgers (DOT) edu

 

 

 

HOME | ABOUT WINLAB | WINLAB RESEARCH | FOCUS PROJECTS | FACULTY | SPONSORSHIP
Copyright © 2004-2012 WINLAB, Rutgers, The State University of New Jersey