Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems

Alessandro Erba, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, Nils Ole Tippenhauer

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

25 Citations (Scopus)
95 Downloads (Pure)


Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process models learned a priori. In this work, we investigate different approaches to evade prior-work reconstruction-based anomaly detectors by manipulating sensor data so that the attack is concealed. We find that replay attacks (commonly assumed to be very strong) show bad performance (i.e., increasing the number of alarms) if the attacker is constrained to manipulate less than 95% of all features in the system, as hidden correlations between the features are not replicated well. To address this, we propose two novel attacks that manipulate a subset of the sensor readings, leveraging learned physical constraints of the system. Our attacks feature two different attacker models: A white box attacker, which uses an optimization approach with a detection oracle, and a black box attacker, which uses an autoencoder to translate anomalous data into normal data. We evaluate our implementation on two different datasets from the water distribution domain, showing that the detector's Recall drops from 0.68 to 0.12 by manipulating 4 sensors out of 82 in WADI dataset. In addition, we show that our black box attacks are transferable to different detectors: They work against autoencoder-, LSTM-, and CNN-based detectors. Finally, we implement and demonstrate our attacks on a real industrial testbed to demonstrate their feasibility in real-time.

Original languageEnglish
Title of host publicationProceedings - 36th Annual Computer Security Applications Conference, ACSAC 2020
PublisherAssociation for Computing Machinery (ACM)
Number of pages16
ISBN (Electronic)9781450388580
Publication statusPublished - 2020
Event36th Annual Computer Security Applications Conference, ACSAC 2020 - Virtual, Online, United States
Duration: 7 Dec 202011 Dec 2020

Publication series

NameACM International Conference Proceeding Series


Conference36th Annual Computer Security Applications Conference, ACSAC 2020
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Accepted Author Manuscript


  • Adversarial Machine Learning
  • Autoencoder
  • Classifier Evasion
  • Deep Learning
  • Evasion Attack
  • Industrial Control System
  • Intrusion Detection
  • Mean Squared Error
  • Multivariate Time Series

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