cs.ROMar 13, 2026

From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks

Authors: Gabriele Gualandi, Alessandro V. Papadopoulos

Organizations: Department of Computer Science and Engineering, Mälardalen University, Västerås, Sweden

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

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\bm{a}_{k} , yielding attacked output y~k\tilde{\bm{y}}_{k} . The Kalman filter generates the innovation rk\bm{r}_{k} , which is monitored by the ADS.
Figure 2: End-effector trajectories: attacker’s reference pˉA\bar{\bm{p}}^{\text{A}} , 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\|\bar{\bm{p}}^{\text{A}}_{k}-\bm{p}_{k}\|_{2} , Mahalanobis distances zkz_{k} and z~k\tilde{z}_{k} of Eqs. 11 and 24 , and scaling f(z~)f(\tilde{z}) of Eq. 25 . With active defence (D), z~k\tilde{z}_{k} increases until scaling becomes effective, then settles, limiting attacker-realizable accelerations.
Figure 4: Adversary and control signals. Optimal increment Δk⋆\bm{\Delta}^{\star}_{k} of Eq. 40 , attack sequence ak\bm{a}_{k} of Eq. 32 , and resulting actuation uk\bm{u}_{k} of Eqs. 9 and 27 . Under the proposed active defence (D), the attacker escalates Δk⋆\bm{\Delta}^{\star}_{k} but scaling suppresses the realized uk\bm{u}_{k} , constraining task-space manipulation.
MetricU (no defence)PO ( χ2\chi^{2} only)D (active defence)
devmax⁡({pˉA},{p})\operatorname{devmax}\bigl(\{\bar{\bm{p}}^{\text{A}}\},\{\bm{p}\}\bigr)0.05{}0.050.28{}0.282.21{}2.21
devmax⁡({pˉ},{p})\operatorname{devmax}\bigl(\{\bar{\bm{p}}\},\{\bm{p}\}\bigr)3.58{}3.583.68{}3.681.5{}1.5
devRMS⁡({pˉA},{p})\operatorname{devRMS}\bigl(\{\bar{\bm{p}}^{\text{A}}\},\{\bm{p}\}\bigr)0.02{}0.020.13{}0.131.42{}1.42
devRMS⁡({pˉ},{p})\operatorname{devRMS}\bigl(\{\bar{\bm{p}}\},\{\bm{p}\}\bigr)2.22{}2.222.2{}2.20.82{}0.82
mean⁡({uk})\operatorname{mean}\bigl(\{\bm{u}_{k}\}\bigr)0.44{}0.440.46{}0.460.06{}0.06
Table I: Task deviation and control effort metrics
More in this topic

Cyber-Physical Systems

Cyber-Physical Systems (CPS) study networked computation and control interacting with physical processes, from factories and power grids to robots and vehicles.

See all 83 papers

Explore similar work

CardsList

  1. Active Defense Against False Data Injection Attacks in Robotic Manipulators

    May 18, 2026Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. PapadopoulosRobotic ControlFalse Data Injection Attacks

    Mälardalen University, Väster˚as, Sweden

  2. Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks

    Apr 13, 2026Axel Andersson, György DánCPS SecurityCyber-Physical Systems

    Department of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden

  3. Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks

    Jun 25, 2026Mohammadreza Nematollahi, Khashayar Khorasani, Nader MeskinCyber-Physical SystemsMulti-Agent Control

    Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. · Department of Electrical Engineering, Qatar University, Doha, Qatar.