  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.
