1,721,002 research outputs found
On model-based detectors for linear time-invariant stochastic systems under sensor attacks
No full textA vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, the authors derive tools for tuning the CUSUM procedure in the fault/attack-free case to fulfil the desired detection performance (in terms of false alarm rate). They use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, they characterise the state degradation that a class of attacks can induce the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, they find the upper bound of state degradation that is possible by an undetected attacker. They quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared), which uses a single measurement at a time. Simulations of a chemical reactor with a heat exchanger are presented to illustrate the performance of their tools
On reachable sets of hidden CPS sensor attacks
No full textFor given system dynamics, observer structure, and observer-based
fault/attack detection procedure, we provide mathematical tools -- in
terms of Linear Matrix Inequalities (LMIs) -- for computing outer
ellipsoidal bounds on the set of estimation errors that attacks can
induce while maintaining the alarm rate of the detector equal to its
attack-free false alarm rate. We refer to these sets to as hidden
reachable sets. The obtained ellipsoidal bounds on hidden reachable sets
quantify the attacker's potential impact when it is constrained to stay
hidden from the detector. We provide tools for minimizing the volume of
these ellipsoidal bounds (minimizing thus the reachable sets) by
redesigning the observer gains. Simulation results are presented to
illustrate the performance of our tools
Performance bounds for optimal feedback control in networks
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).Many important complex networks, including critical infrastructure and emerging industrial automation systems, are becoming increasingly intricate webs of interacting feedback control loops. A fundamental concern is to quantify the control properties and performance limitations of the network as a function of its dynamical structure and control architecture. We study performance bounds for networks in terms of optimal feedback control costs. We provide a set of complementary bounds as a function of the system dynamics and actuator structure. For unstable network dynamics, we characterize a tradeoff between feedback control performance and the number of control inputs, in particular showing that optimal cost can increase exponentially with the size of the network. We also derive a bound on the performance of the worst-case actuator subset for stable networks, providing insight into dynamics properties that affect the potential efficacy of actuator selection. We illustrate our results with numerical experiments that analyze performance in regular and random networks. ©2018 AACC.Army Research Office under Grant Number: W911NF-17-1-0058.Erik Jonsson School of Engineering and Computer Scienc
A comparison of stealthy sensor attacks on control systems
No full textAs more attention is paid to security in the context of control systems
and as attacks occur to real control systems throughout the world, it
has become clear that some of the most nefarious attacks are those that
evade detection. The term stealthy has come to encompass a variety of
techniques that attackers can employ to avoid detection. Here we show
how the states of the system (in particular, the reachable set
corresponding to the attack) can be manipulated under two important
types of stealthy attacks. We employ the chi-squared fault detection
method and demonstrate how this imposes a constraint on the attack
sequence either to generate no alarms (zero-alarm attack) or to generate
alarms at a rate indistinguishable from normal operation (hidden
attack)
Tuning windowed chi-squared detectors for sensor attacks
No full textA model-based windowed chi-squared procedure is proposed for identifying
falsified sensor measurements. We employ the widely-used static
chi-squared and the dynamic cumulative sum (CUSUM) fault/attack
detection procedures as benchmarks to compare the performance of the
windowed chi-squared detector. In particular, we characterize the state
degradation that a class of attacks can induce to the system while
enforcing that the detectors do not raise alarms (zero-alarm attacks).
We quantify the advantage of using dynamic detectors (windowed
chi-squared and CUSUM detectors), which leverages the history of the
state, over a static detector (chi-squared) which uses a single
measurement at a time. Simulations using a chemical reactor are
presented to illustrate the performance of our tools
Constraining attacker capabilities through actuator saturation
No full textFor LTI control systems, we provide mathematical tools - in terms of
Linear Matrix Inequalities - for computing outer ellipsoidal bounds on
the reachable sets that attacks can induce in the system when they are
subject to the physical limits of the actuators. Next, for a given set
of dangerous states, states that (if reached) compromise the integrity
or safe operation of the system, we provide tools for designing new
artificial limits on the actuators (smaller than their physical bounds)
such that the new ellipsoidal bounds (and thus the new reachable sets)
are as large as possible (in terms of volume) while guaranteeing that
the dangerous states are not reachable. This guarantees that the new
bounds cut as little as possible from the original reachable set to
minimize the loss of system performance. Computer simulations using a
platoon of vehicles are presented to illustrate the performance of our
tools
Robustness of Real Network Controllability to Degree Based Attacks
No full textReal world complex networks vary greatly topologically from each other as well as from
generated synthetic random networks. For example, social and biological networks typically
have a community structure, while random networks do not. In order to investigate the
robustness of the controllability of real networks to attacks on its edges, five different attacks
based on the degree of the nodes were levied at common real-world networks systematically by
removing either 2% or 5% of the edges, in steps, until 90% were removed. It was then
investigated how well these real networks retain their controllability, especially in comparison to
Erdos-Renyi and Barabasi-Albert synthetic networks. In particular, the question of how effective
attacks focusing on destroying edges with a high source node in-degree and a high target node
out-degree, performed in comparison to attacks focused on edges with high betweenness
centrality, was reviewed. It was discovered, that in contrast to results with synthetic networks,
for many real networks the betweenness attack performed worse than the in-out attack after a
certain number of edges were removed. By observing how high density and community structure
affect the ability to retain control over the network after these two attacks, an explanation for this may be assembled. In addition, the difference between the potency of these two attacks, while network controls were fixed to nodes or allowed to move to more optimal input nodes, was studied
RC Circuit Model-Based Anomaly Detection for Li-ion Batteries
No full textWith the increased use of Lithium ion batteries in a variety of applications, the presence
of an anomaly proves to be a major concern as it not only affects the battery, but also
affects the battery operated system. Battery Management System (BMS) can be equipped
with various anomaly detection procedures to detect failures and attacks and hence prevent
improper functioning and catastrophic events caused by such anomalies. In this research,
the Lithium ion battery is modeled into a first order RC equivalent circuit to understand its
behavior. Kalman filter is used to estimate the states and an adaptive estimation algorithm
is used to estimate the model parameters. Residual based detection mechanism is employed
for anomaly detection. By understanding the performance of the detectors and comparing
them with each other, they are tuned to detect the zero-alarm attacks which equip them for
worst-case attack detection
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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