sundaslatif/ics-evasion-attacks

Public Code for ICS Evasion Attack Generation

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

In proceedings of the the Annual Computer Security Applications Conference (ACSAC)

When using this code from this repository please cite our work as follows:

@InProceedings{erba2020concealment,
  title={Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors 
  in Industrial Control Systems},
  author={Erba, Alessandro and Taormina, Riccardo and Galelli, Stefano and
   Pogliani, Marcello and Carminati, Michele and Zanero, Stefano and
   Tippenhauer, Nils Ole},
  booktitle={Proceedings of the Annual Computer Security Applications Conference 
  (ACSAC)},
  year={2020},
  doi = {10.1145/3427228.3427660}
}

Implementation of iterative and learning-based concealment attacks, artifact evaluated Functional by the ACSAC 2020 artifact evaluation committee

Description

This repository is organized as follows:

• Attacked_Model contains the Autoencoder based detector trained on BATADAL and WADI data. The code was forked from AutoEncoders for Event Detection (AEED) and complemented with LST and CNN defenses. Please refer to the original repository to train your defense model.

• Adversarial_Attacks contains the iterative and learning-based concealment attacks implementations used for the evaluation of our manuscript.

• Data contains the dataset used for the experiment. Note WADI data are available upon request at iTrust

• Evaluation contains the python notebooks to evaluate the attack efficacy in different constraints scenarios. By running each notebook you reporduce the results found in the paper and the relative plots (i.e. Figures 2 and 3 in the manuscript). To open jupyter run the following commad conda activate && jupyter notebook and then open the .ipynb files in Evauation folder

Requirements

To execute attacks and evaluate them Python 3, Keras, Pandas, Numpy, Jupyter are required. Installation through conda is suggested.

Usage

Command-line options available:

-d 'dataset'data to be used for the attacks, default=BATADAL

Whitebox_Attack folder:

unconstrained_attack.py

It performs the unconstrained iterative attack in the white box setting.

Usage:

python unconstrained_attack.py -d BATADAL

constrained_attack_constraints_extraction.py

Extract the constraints for the best-case scenario constrained attack.

Usage:

python constrained_attack_constraints_extraction.py -d BATADAL

constrained_attack.py

It performs the best-case scenario constrained attack, and applies the constraints found with constrained_attack_constraints_extraction.py.

Usage:

python constrained_attack.py -d BATADAL

constrained_attack_PLC.py

It performs the topology-based scenario constrained attack, and applies the constraints according to the PLC controlled by the attacker.

Optional Parameters:

• -a 'attack id' attack id that we want to conceal

• -p 'PLC_N' PLC controlled by the attacker

Usage:

python constrained_attack_PLC.py -d BATADAL -a 1 -p PLC_1

launcher.py

util to automate and parallelize topology-based scenario constrained attacks.

Usage:

python launcher.py -d BATADAL

Black_Box_Attack folder:

Optional Parameter:

• -p 'Bool' train Adversarial Autoencoder network, default=False (All trained models are already available in the repository)

unconstrained_attack.py

It performs the unconstrained learning-based attack in the black box setting.

Optional Parameters:

• -t 'Bool', measure time for concealment attack, default=False

Usage:

python unconstrained_attack.py -d BATADAL

constrained_attack_X_dimension.py

Partially constrained attack best-case constraints

Optional Parameters:

• -f 'Bool', default=True. If False extract the constraints for the best-case scenario constrained attack. (All saved constraints are available in the repository)

Usage:

python constrained_attack_X_dimension.py -d BATADAL -p False -f True

constrained_attack_X_dimension_PLC.py

Partially constrained attack topology-based constraints script, X Dimension, PLC constraints

Usage:

python constrained_attack_X_dimension_PLC.py -d BATADAL -p False

constrained_AE_attack_X_dimension_PLC.py

Fully constrained attack script, topology-based constraints, X Dimension, PLC constraints

Usage:

python constrained_AE_attack_X_dimension_PLC.py -d BATADAL -p False

constrained_attack_D_dimension.py

Constrained attack, D dimension

Usage:

python constrained_attack_D_dimension.py -d BATADAL -p False

Replay_Attack folder:

replay_attack.py

Optional Parameters:

• -c 'topology'/'best' constraints to be applied scenario topology or best case scenario, default=best

Usage:

python replay_attack.py -d BATADAL -c best