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April 2014

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*_Notice and Invitation_*

Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University

*Andre Bernard Abadie*
Bachelor of Science , U.S. Military Academy, 1996**
Master of Science, University of Maryland University College, 2008
Master of Military Art and Science, U.S. Army Command and General Staff 
College, 2009
Master of Science, National Defense University, 2013

*Combining Operational and Spectrum Characteristics to Form a Risk Model 
for Positive Train Control Communications***

Thursday, April 24, 2014, 1pm
Room 401, Research I

All are invited to attend.

*_Committee_*
Duminda Wijesekera, Chair
Angelos Stavrou
Robert Simon
David Schum

*_Abstract_*

Though there is a tremendous research effort towards maturing software 
defined radio (SDR) to cognitive radio technologies, it is narrowly 
focused on spectrum access or performance tuning. An alternative 
approach is to create a cognition cycle that facilitates risk management 
and improves the radio's security posture. In the instances where SDR 
technologies represent a critical infrastructure, this advancement would 
be novel and paramount. SDR technologies represent the future of radio 
infrastructures due to their ability to establish flexibility -- the 
potential for users to adjust performance capability in their 
ever-changing operational settings. Yet with this transition to 
software, radios will face similar threats as current computer systems. 
Therefore it is necessary to begin formulating risk models that can 
assess this new risk environment. Though the threats are not known at 
this time, the risks they may present can be estimated and addressed.

As mandated by the Rail Safety Improvement Act of 2008, trains are 
incorporating SDR technologies in their implementation of Positive Train 
Control (PTC) -- a distributed system enhancing the train's 
communications with the command and control infrastructure, railway 
switches, and crossings to address the numerous safety considerations in 
rail operations. There are risk models for rail operations and PTC is 
designed to ensure high-risk environments are avoided or mitigated 
through adjustments in the operation of the train (assuming they receive 
the associated PTC status message).

This dissertation introduces a risk engine that can be incorporated as a 
cognition cycle in PTC architectures currently under development. It 
provides awareness of heightened risk to the communication system 
availability based on environmental factors and, more importantly, 
adversarial attempts to exploit vulnerabilities in SDR technologies. To 
predict its performance in the intended operational environment, the 
model is simulated against train operations within a major urban 
environment and a regional expanse to demonstrate its ability to assess 
risk under varied conditions. To increase its relevance to the original 
intent of PTC -- prevention of accidents -- it is merged with an 
existing safety risk model in order to deliver risk assessments 
applicable to the situations where PTC is needed most. Findings are 
discussed to assess potential effectiveness as well as highlight 
challenges. A discussion of its relevance as a contribution to the 
research community is highlighted, specifically: risk estimates of 
planned rail operations, design and network planning of future PTC 
requirements, and initial efforts for adversary detection. The 
dissertation concludes with significant aspects of further research.

A copy of this doctoral dissertation is on reserve at the Johnson Center 
Library.



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