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Cited by 22Smart Grid Security and ResilienceNetwork Security and Intrusion DetectionSecurity and Verification in Computing

Fingerprinting movements of industrial robots for replay attack detection

Pu, Hongyi, He, Liang, Zhao, Chengcheng, Yau, David K. Y., Cheng, Peng, Chen, Jiming·2022·IEEE Transactions on Mobile Computing
DOI 10.1109/tmc.2021.3059796
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Abstract

Industrial robots are prototypical cyber-physical systems widely deployed in (smart) manufacturing, which operate according to the operation code uploaded by the human operator and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, calledPIDS(Power-basedIntrusionDetectionSystem), and deliverPIDSas abump-in-the-wiremodule installed at the powerline of commodity robots. The foundation ofPIDSis the physically-induced dependency between the robot movement and the concomitant power consumption, whichPIDScaptures via joint physical analysis and (cyber) data-driven modeling.PIDSthen fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information — a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluatedPIDSusing three models of robots from different vendors — i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots — with over 2,000 operation cycles. Experimental results show thatPIDSdetects replay attacks at an average rate of 96.5 percent (up to 99.9 percent) and a 0.1s latency.

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Crossref · 10.1109/tmc.2021.3059796

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  • Pu et al. [146]'s system is based on the physical principle that a specific commanded motion should consume a predictable amount of power, and it learns energy-consistency patterns and detects replay attacks by identifying discrepancies between the monitored trajectory and the actual power consumption.

    “Based on the physical principle that a specific commanded motion should consume a predictable amount of power, the system learns energyconsistency patterns and detects replay attacks by identifying discrepancies between the monitored trajectory and the actual power consumption.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [146]

  • Pu et al. [146] introduced PIDS, a detection framework that fingerprints the physically induced dependency between robot motion and corresponding power consumption, using an industrial robot testbed.

    “Using an industrial robot testbed, Pu et al. [146] introduced PIDS, a detection framework that fingerprints the physically induced dependency between robot motion and corresponding power consumption.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [146]

  • Other-data-type-driven anomaly detection studies deliberately move beyond conventional network and process data to monitor artifacts that are closer to the root cause of cyber-physical compromise-such as PLC control logic structure, acoustic side-channel signals, host-level physical resource usage, and power-consumption traces-thereby covering attack surfaces that packet- or sensor-centric dete

    “First, they deliberately move beyond conventional network and process data to monitor artifacts that are closer to the root cause of cyber-physical compromise-such as PLC control logic structure, acoustic side-channel signals, host-level physical resource usage, and power-consumption traces-thereby covering attack surfaces that packet- or sensor-centric detectors inherently miss [142-146].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [146]

  • Most other-data-type approaches are designed as lightweight, mostly unsupervised endpoint monitors that can operate on resource-constrained devices or external side channels without requiring deep modifications to existing control networks, suggesting a practical way to complement network- and process-based IDS in real deployments.

    “Third, most approaches are designed as lightweight, mostly unsupervised endpoint monitors that can operate on resource-constrained devices or external side channels without requiring deep modifications to existing control networks, suggesting a practical way to complement network- and process-based IDS in real deployments [142,145,146].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [146]

  • This makes it possible to detect semantically stealthy or encrypted attacks by flagging violations of these auxiliary consistency relations, even when primary cyber channels are partially hidden or manipulated.

    “This makes it possible to detect semantically stealthy or encrypted attacks by flagging violations of these auxiliary consistency relations, even when primary cyber channels are partially hidden or manipulated [142,144-146].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [146]

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Related

Authors
Pu, H., He, L., Zhao, C., Yau, D. K. Y., Cheng, P., & Chen, J.
Affiliations
  • Zhejiang University (CN)
  • University of Colorado Denver (US)
  • Singapore University of Technology and Design (SG)
ORCID
Venue
IEEE Transactions on Mobile Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-10-01
Volume
21
Issue
10
Pages
3629-3643
ISSN
  • 1536-1233 (ISSN-L)
  • 1558-0660
  • 2161-9875
DOI
10.1109/tmc.2021.3059796
OpenAlex
W3129764251
Cited by
22
References
23
Citations by year
2026: 2 · 2025: 3 · 2024: 5 · 2023: 9 · 2022: 2 · 2021: 1
FWCI
1.89
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Mobile Computing (publishedVersion)
Funders
  • Science and Technology Innovation 2030 program (2018AAA0101605)
  • National Natural Science Foundation of China (U1911401, 61833015, 61903328)
  • Fundamental Reseach Funds for the Central Universities
  • SUTD-ZJU IDEA (201805)
  • Singapore MOE (SUTDT12017004)
  • University of Colorado Denver
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
Keywords
industrial robots, replay attacks, intrusion detection, cyber-physical security, anomaly detection, smart manufacturing

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