(2020) Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems.
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Abstract
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 whitebox attacker, which uses an optimization approach with a detection oracle, and a blackbox 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 blackbox 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.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Nils Ole Tippenhauer (SCy-Phy) |
| Conference: | ACSAC Annual Computer Security Applications Conference |
| Depositing User: | Nils Ole Tippenhauer |
| Date Deposited: | 25 Sep 2020 10:04 |
| Last Modified: | 31 Oct 2020 07:33 |
| Primary Research Area: | NRA3: Threat Detection and Defenses |
| URI: | https://publications.cispa.saarland/id/eprint/3226 |
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