| dc.description.abstract | This dissertation studies two important topics regarding the resiliency and the security
of cyber-physical systems (CPSs). In the first work, a self-healing graphical representation
is proposed to study the contagion of failures in self-healing interdependent networks.
To this end, a graphical model representation of an interdependent cyber-physical system is
proposed, in which nodes denote various cyber or physical functionalities, and edges capture
the interactions between nodes. Then, a message-passing (belief propagation) algorithm is
applied to this representation in order to analyze network reactions to initial disruptions.
The framework is then extended to cases where the propagation of failures in the physical
network is faster than the healing responses of the cyber network. Such scenarios are of
interest in many real-life applications, such as the smart grid. As a result, it is proven that
as the number of message-passing iterations increases, the network reaches a steady-state
condition that would be either a complete healing or a complete collapse. The findings from
this analysis help network designers have a better understanding of the resiliency of CPSs.
In the second work, security measurement and the malicious node detection of autonomous
vehicles in intelligent transportation systems are studied. First, a simple security
model based on Bayesian defense graphs is proposed to quantitatively assess the likelihood
of threats against autonomous vehicles (AVs) in the presence of available countermeasures.
Then, a game-theoretic model is represented using a local voting-based game to detect misbehaving
neighboring vehicles in places where centrally managed stations are absent. In order
to capture the inherent uncertainty of vehicles in ephemeral vehicular networks, a Bayesian
game is used in which malicious nodes can potentially impact the result of the game. Then,
equilibria of this game are obtained to study the strategies of malicious and benign nodes in
networks. Using the analysis, the game parameters can be designed to achieve the maximum
performance of misbehavior detection in vehicular networks. | |
