  In a connected transportation system, adaptive traffic signal controllers
(ATSC) utilize real-time vehicle trajectory data received from vehicles through
wireless connectivity (i.e., connected vehicles) to regulate green time.
However, this wirelessly connected ATSC increases cyber-attack surfaces and
increases their vulnerability to various cyber-attack modes, which can be
leveraged to induce significant congestion in a roadway network. An attacker
may receive financial benefits to create such a congestion for a specific
roadway. One such mode is a 'sybil' attack in which an attacker creates fake
vehicles in the network by generating fake Basic Safety Messages (BSMs)
imitating actual connected vehicles following roadway traffic rules. The
ultimate goal of an attacker will be to block a route(s) by generating fake or
'sybil' vehicles at a rate such that the signal timing and phasing changes
occur without flagging any abrupt change in number of vehicles. Because of the
highly non-linear and unpredictable nature of vehicle arrival rates and the
ATSC algorithm, it is difficult to find an optimal rate of sybil vehicles,
which will be injected from different approaches of an intersection. Thus, it
is necessary to develop an intelligent cyber-attack model to prove the
existence of such attacks. In this study, a reinforcement learning based
cyber-attack model is developed for a waiting time-based ATSC. Specifically, an
RL agent is trained to learn an optimal rate of sybil vehicle injection to
create congestion for an approach(s). Our analyses revealed that the RL agent
can learn an optimal policy for creating an intelligent attack.
