  An adaptive traffic signal controller (ATSC) combined with a connected
vehicle (CV) concept uses real-time vehicle trajectory data to regulate green
time and has the ability to reduce intersection waiting time significantly and
thereby improve travel time in a signalized corridor. However, the CV-based
ATSC increases the size of the surface vulnerable to potential cyber-attack,
allowing an attacker to generate disastrous traffic congestion in a roadway
network. An attacker can congest a route by generating fake vehicles by
maintaining traffic and car-following rules at a slow rate so that the signal
timing and phase change without having any abrupt changes in number of
vehicles. Because of the adaptive nature of ATSC, it is a challenge to model
this kind of attack and also to develop a strategy for detection. This paper
introduces an innovative "slow poisoning" cyberattack for a waiting time based
ATSC algorithm and a corresponding detection strategy. Thus, the objectives of
this paper are to: (i) develop a "slow poisoning" attack generation strategy
for an ATSC, and (ii) develop a prediction-based "slow poisoning" attack
detection strategy using a recurrent neural network -- i.e., long short-term
memory model. We have generated a "slow poisoning" attack modeling strategy
using a microscopic traffic simulator -- Simulation of Urban Mobility (SUMO) --
and used generated data from the simulation to develop both the attack model
and detection model. Our analyses revealed that the attack strategy is
effective in creating a congestion in an approach and detection strategy is
able to flag the attack.
