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.