3 research outputs found
Detection and Mitigation of Attacks on Transportation Networks as a Multi-Stage Security Game
In recent years, state-of-the-art traffic-control devices have evolved from
standalone hardware to networked smart devices. Smart traffic control enables
operators to decrease traffic congestion and environmental impact by acquiring
real-time traffic data and changing traffic signals from fixed to adaptive
schedules. However, these capabilities have inadvertently exposed traffic
control to a wide range of cyber-attacks, which adversaries can easily mount
through wireless networks or even through the Internet. Indeed, recent studies
have found that a large number of traffic signals that are deployed in practice
suffer from exploitable vulnerabilities, which adversaries may use to take
control of the devices. Thanks to the hardware-based failsafes that most
devices employ, adversaries cannot cause traffic accidents directly by setting
compromised signals to dangerous configurations. Nonetheless, an adversary
could cause disastrous traffic congestion by changing the schedule of
compromised traffic signals, thereby effectively crippling the transportation
network. To provide theoretical foundations for the protection of
transportation networks from these attacks, we introduce a game-theoretic model
of launching, detecting, and mitigating attacks that tamper with traffic-signal
schedules. We show that finding optimal strategies is a computationally
challenging problem, and we propose efficient heuristic algorithms for finding
near optimal strategies. We also introduce a Gaussian-process based anomaly
detector, which can alert operators to ongoing attacks. Finally, we evaluate
our algorithms and the proposed detector using numerical experiments based on
the SUMO traffic simulator
A Minimax Game for Resilient-by-design Adaptive Traffic Control Systems
Connected and Autonomous Vehicles (CAVs) with their evolving data gathering
capabilities will play a significant role in road safety and efficiency
applications supported by Intelligent Transport Systems (ITS), such as Adaptive
Traffic Signal Control (ATSC) for urban traffic congestion management. However,
their involvement will expand the space of security vulnerabilities and create
larger threat vectors. We perform the first detailed security analysis and
implementation of a new cyber-physical attack category carried out by the
network of CAVs on ITS, namely, coordinated Sybil attacks, where vehicles with
forged or fake identities try to alter the data collected by the ATSC
algorithms to sabotage their decisions. Consequently, a novel, game-theoretic
mitigation approach at the application layer is proposed to minimize the impact
of Sybil attacks. The devised minimax game model enables the ATSC algorithm to
generate optimal decisions under a suspected attack, improving its resilience.
Extensive experimentation is performed on a traffic dataset provided by the
City of Montreal under real-world intersection settings to evaluate the attack
impact. Our results improved time loss on attacked intersections by
approximately 48.9%. Substantial benefits can be gained from the mitigation,
yielding more robust control of traffic across networked intersections
Security Risk Analysis of the Shorter-Queue Routing Policy for Two Symmetric Servers
In this article, we study the classical shortest queue problem under the
influence of malicious attacks, which is relevant to a variety of engineering
system including transportation, manufacturing, and communications. We consider
a homogeneous Poisson arrival process of jobs and two parallel exponential
servers with symmetric service rates. A system operator route incoming jobs to
the shorter queue; if the queues are equal, the job is routed randomly. A
malicious attacker is able to intercept the operator's routing instruction and
overwrite it with a randomly generated one. The operator is able to defend
individual jobs to ensure correct routing. Both attacking and defending induce
technological costs. The attacker's (resp. operator's) decision is the
probability of attacking (resp. defending) the routing of each job. We first
quantify the queuing cost for given strategy profiles by deriving a theoretical
upper bound for the cost. Then, we formulate a non-zero-sum attacker-defender
game, characterize the equilibria in multiple regimes, and quantify the
security risk. We find that the attacker's best strategy is either to attack
all jobs or not to attack, and the defender's strategy is strongly influenced
by the arrival rate of jobs. Finally, as a benchmark, we compare the security
risks of the feedback-controlled system to a corresponding open-loop system
with Bernoulli routing. We show that the shorter-queue policy has a higher
(resp. lower) security risk than the Bernoulli policy if the demand is lower
(resp. higher) than the service rate of one server