Cyber-physical robotic systems are vulnerable to false data injection attacks (FDIAs), in which an adversary corrupts sensor signals while evading residual-based passive anomaly detectors such as the chi-squared test. Such stealthy attacks can induce substantial end-effector deviations without triggering alarms. This paper studies the resilience of redundant manipulators to stealthy FDIAs and advances the architecture from passive monitoring to active defence. We formulate a closed-loop model comprising a feedback-linearized manipulator, a steady-state Kalman filter, and a chi-squared-based anomaly detector. Building on this passive monitoring layer, we propose an active control-level defence that attenuates the control input through a monotone function of an anomaly score generated by a novel actuation-projected, measurement-free state predictor. The proposed design provides probabilistic guarantees on nominal actuation loss and preserves closed-loop stability. From the attacker perspective, we derive a convex QCQP for computing one-step optimal stealthy attacks. Simulations on a 6-DOF planar manipulator show that the proposed defence significantly reduces attack-induced end-effector deviation while preserving nominal task performance in the absence of attacks.
Figures & tables
Figure 1: Closed-loop system under FDIA. Sensor signals are corrupted by injection ak , yielding attacked output y~k . The Kalman filter generates the innovation rk , which is monitored by the ADS.
Figure 2: End-effector trajectories: attacker’s reference pˉA , defended only by the passive detector (PO) and by the proposed active defence (D). The latter defence limits drift toward the malicious target.
Figure 3: Attack tracking error ∥pˉkA−pk∥2 , Mahalanobis distances zk and z~k of Eqs. 11 and 24 , and scaling f(z~) of Eq. 25 . With active defence (D), z~k increases until scaling becomes effective, then settles, limiting attacker-realizable accelerations.
Figure 4: Adversary and control signals. Optimal increment Δk⋆ of Eq. 40 , attack sequence ak of Eq. 32 , and resulting actuation uk of Eqs. 9 and 27 . Under the proposed active defence (D), the attacker escalates Δk⋆ but scaling suppresses the realized uk , constraining task-space manipulation.
Metric
U (no defence)
PO ( χ2 only)
D (active defence)
devmax({pˉA},{p})
0.05
0.28
2.21
devmax({pˉ},{p})
3.58
3.68
1.5
devRMS({pˉA},{p})
0.02
0.13
1.42
devRMS({pˉ},{p})
2.22
2.2
0.82
mean({uk})
0.44
0.46
0.06
Table I: Task deviation and control effort metrics
Cyber-Physical Systems (CPS) study networked computation and control interacting with physical processes, from factories and power grids to robots and vehicles.
Robotic systems are vulnerable to False Data Injection Attacks (FDIAs), where adversaries corrupt sensor signals to gain malicious control. Feedback linearization exposes robotic systems to integrator vulnerability, making them susceptible to stealthy attacks that can cause significant deviations in end-effector behavior without raising alarms. This paper addresses the resilience of manipulators against finite-horizon FDIAs by formalizing two defense methods, namely anomaly-aware virtual damping and manipulability reduction, with probabilistic guarantees on nominal task execution. Simulations on a 7-DOF redundant manipulator show that the proposed defenses substantially reduce the impact of FDIA compared to using solely a threshold-based ADS like the Chi-squared, while preserving nominal task performance in the absence of attack.
Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. Papadopoulos
We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern-day, complex perception pipelines as bipartite graphs, which combined with anomaly detector alerts defines a Bayesian network for inferring compromised sensors. An active probing strategy exploits system nonlinearities to maximize distinguishability between attack hypotheses, while compromised sensors are selectively disabled to maintain reliable state estimation. We propose a threshold-based probing strategy and show its effectiveness via a simplified partially observable Markov decision process (POMDP) formulation. Experiments on an inverted pendulum under single and multi-sensor attacks show that our method significantly outperforms outlier-robust and prediction-based baselines, especially under prolonged attacks.
Axel Andersson, György Dán
Department of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden
This work studies resilient output containment for heterogeneous linear multi-agent systems with actuator cyber-attacks over directed network topologies. The leaders generate bounded locally absolutely continuous trajectories; however, their dynamics, velocity bounds, and motion envelopes are undisclosed to the followers. The cyber-attack model includes state- and input-correlated, as well as bounded exogenous actuator false-data terms. A continuous two-layer adaptive control architecture is proposed. The first layer is a virtual-actuator reconfiguration layer that uses partial state measurements to compensate for actuator attacks in the local tracking-error dynamics. The second layer is a network interface that generates task-space commands via an adaptive interaction protocol. This protocol uses only neighbor-exchanged network-interface states whose dimensions match those of the plant output, and it does not require global graph knowledge for parameter tuning. For directed graphs, under a leader-rooted united spanning-tree condition, a nonsmooth Lyapunov analysis yields asymptotic containment at the command level. The physical outputs then converge to the leader convex hull up to a residual determined by the command-tracking local controllers. Simulation results using a network of quadrotors with damped suspended loads illustrate the performance of attack recovery and containment tracking.
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. · Department of Electrical Engineering, Qatar University, Doha, Qatar.