1,834 research outputs found
Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems
This paper develops a glocal (global-local) attack detection framework to
detect stealthy cyber-physical attacks, namely covert attack and zero-dynamics
attack, against a class of multi-agent control systems seeking average
consensus. The detection structure consists of a global (central) observer and
local observers for the multi-agent system partitioned into clusters. The
proposed structure addresses the scalability of the approach and the privacy
preservation of the multi-agent system's state information. The former is
addressed by using decentralized local observers, and the latter is achieved by
imposing unobservability conditions at the global level. Also, the
communication graph model is subject to topology switching, triggered by local
observers, allowing for the detection of stealthy attacks by the global
observer. Theoretical conditions are derived for detectability of the stealthy
attacks using the proposed detection framework. Finally, a numerical simulation
is provided to validate the theoretical findings.Comment: to appear in IEEE CD
Bibliographical review on cyber attacks from a control oriented perspective
This paper presents a bibliographical review of definitions, classifications and applications concerning cyber attacks in networked control systems (NCSs) and cyber-physical systems (CPSs). This review tackles the topic from a control-oriented perspective, which is complementary to information or communication ones. After motivating the importance of developing new methods for attack detection and secure control, this review presents security objectives, attack modeling, and a characterization of considered attacks and threats presenting the detection mechanisms and remedial actions. In order to show the properties of each attack, as well as to provide some deeper insight into possible defense mechanisms, examples available in the literature are discussed. Finally, open research issues and paths are presented.Peer ReviewedPostprint (author's final draft
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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