487,840 research outputs found
A Formal Approach to Cyber-Physical Attacks
We apply formal methods to lay and streamline theoretical foundations to
reason about Cyber-Physical Systems (CPSs) and cyber-physical attacks. We focus
on %a formal treatment of both integrity and DoS attacks to sensors and
actuators of CPSs, and on the timing aspects of these attacks. Our
contributions are threefold: (1) we define a hybrid process calculus to model
both CPSs and cyber-physical attacks; (2) we define a threat model of
cyber-physical attacks and provide the means to assess attack
tolerance/vulnerability with respect to a given attack; (3) we formalise how to
estimate the impact of a successful attack on a CPS and investigate possible
quantifications of the success chances of an attack. We illustrate definitions
and results by means of a non-trivial engineering application
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
We show that end-to-end learning of communication systems through deep neural
network (DNN) autoencoders can be extremely vulnerable to physical adversarial
attacks. Specifically, we elaborate how an attacker can craft effective
physical black-box adversarial attacks. Due to the openness (broadcast nature)
of the wireless channel, an adversary transmitter can increase the
block-error-rate of a communication system by orders of magnitude by
transmitting a well-designed perturbation signal over the channel. We reveal
that the adversarial attacks are more destructive than jamming attacks. We also
show that classical coding schemes are more robust than autoencoders against
both adversarial and jamming attacks. The codes are available at [1].Comment: to appear at IEEE Communications Letter
Learning-based attacks in cyber-physical systems
We introduce the problem of learning-based attacks in a simple abstraction of
cyber-physical systems---the case of a discrete-time, linear, time-invariant
plant that may be subject to an attack that overrides the sensor readings and
the controller actions. The attacker attempts to learn the dynamics of the
plant and subsequently override the controller's actuation signal, to destroy
the plant without being detected. The attacker can feed fictitious sensor
readings to the controller using its estimate of the plant dynamics and mimic
the legitimate plant operation. The controller, on the other hand, is
constantly on the lookout for an attack; once the controller detects an attack,
it immediately shuts the plant off. In the case of scalar plants, we derive an
upper bound on the attacker's deception probability for any measurable control
policy when the attacker uses an arbitrary learning algorithm to estimate the
system dynamics. We then derive lower bounds for the attacker's deception
probability for both scalar and vector plants by assuming a specific
authentication test that inspects the empirical variance of the system
disturbance. We also show how the controller can improve the security of the
system by superimposing a carefully crafted privacy-enhancing signal on top of
the "nominal control policy." Finally, for nonlinear scalar dynamics that
belong to the Reproducing Kernel Hilbert Space (RKHS), we investigate the
performance of attacks based on nonlinear Gaussian-processes (GP) learning
algorithms
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