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poisoning-ics-ad

Official implementation of "Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems" by Moshe Kravchik, Battista Biggio, and Asaf Shabtai, SAC 2021.

Requirements

  • Python 3.6
  • tensorflow==1.15
  • Keras==2.2.4
  • munch==2.5.0
  • numpy==1.18.1
  • scikit-image==0.16.2
  • scikit-learn==0.22.1
  • scipy==1.4.1

For running the SWaT tests the code is expecting to have the dataset that can be requested at https://itrust.sutd.edu.sg/itrust-labs_datasets/. The dataset train and test files should be subsampled at the 5 seconds rate and saved locally in the files named SWaT_Dataset_Normal_sub5.csv and SWaT_Dataset_Attack_sub5.csv, correspondingly.

Usage

python Poisoning.py [-h] [-c CONFIGURATION] [-a {3,7,16,31,32,33,36}] {syn,swat}

For training and poisoning using the synthetic data run:

python Poisoning.py syn

For training and poisoning using the SWaT data run:

python Poisoning.py swat [-a {3,7,16,31,32,33,36}]

The are multiple configuration parameters defined in the conf_syn.py and conf_swat.py files. You can tweak them to experiment with different settings. The meaning of the non-standard ones is explained in the article.

Parameters (in the alphabetic order)

  • activate_last {True, False} - Use activation function in the last layer. Using it increases the model's robustness, but might also limit the maximal possible attack on the model.
  • activation - The activation function used in the model.
  • adv_it_count - The number of iterations of a single back-gradient algorithm run.
  • adv_lr - The learning rate for the poison updates used in the back-gradient algorithm.
  • att_len - The attack duration (in time steps).
  • att_magn - The attack magnitude.
  • att_point - {"CUSTOM_FIXED"|"CUSTOM_LINEAR"|"SIN_TOP"|"SIN_BOTTOM"|"SIN_SIDE"}. The attack location on the signal's period.
  • batches - The number of batches in the model's training.
  • code_ratio - The proportion between the input and the model's bottleneck dimensions.
  • find_poison {True, False} - Use optimization to find the maximal starting poison that does not trigger an alert.
  • generator {SinGenerator|DoubleSinGenerator} - The synthetic signal generator. Defined in generators.py.
  • inflate_factor - The factor of the model's inflation layer.
  • it_count - Model's training iteration count.
  • layers - The number of layers in the model (encoder and decoders separately).
  • lr - The model's training learning rate.
  • max_adv_iter - The maximal number of poisoning iterations.
  • max_clean_poison - Not used.
  • naive {True, False} - The algorithm to use: interpolation(True) or back-gradient.
  • optimizer - The model's training optimizer.
  • partial_attacked - The indices of features to attack.
  • periods - The number of periods to include in the synthesized signal.
  • randomize {True, False} - Use random shuffling when training the model.
  • retrain - Not used.
  • retrain_points - Not used.
  • sec_seq_len - If present, the second model's sequence length.
  • seq_len - The model sequence length.
  • signal_func - The signal function used by the generators.
  • silent {True|False} - The output verbosity.
  • single_sequence {False|True} - Model the entire signal at ones or as short overlapping sequences.
  • threshold - The attack detection threshold.
  • total_len - Don't set, is overwritten by the code.
  • train_points - The number of training batches for the synthetic data; for the SWaT attacks, controls how long is the signal used for the model's training (measured in the signal's period).
  • window - The attack detection window.