Adversarial Robustness with Non-uniform Perturbations

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Miscellaneous adversarial-robustness-with-nonuniform-perturbations
Overview

Adversarial Robustness with Non-uniform Perturbations

This repository hosts the code to replicate experiments of the paper Adversarial Robustness with Non-uniform Perturbations for malware use-case.

Installation

Please first refer to https://github.com/elastic/ember and follow the instructions to download 2018 version of EMBER dataset and install ember package.

After installing ember, use pip to install the required packages for this project:

pip install -r requirements.txt

Usage

NonuniformRobustness.py

This file contains the following functions:

  • get_clean_data(data_dir) returns train and test sets of vectorized EMBER dataset features

  • get_adversarial_examples(data_dir, num_features, attack_name="GreedyNN") returns adversarial example set which is crafted by the specified problem space attack, e.g., Greedy attack (GreedyNN).

  • omega(num_features, perturbation_constraint="Uniform") computes the matrix Omega which is used in l2 PGD algorithm as a coefficient of delta perturbation constraint. Constraint type can be selected as "Uniform", "Pearson", "SHAP","Masked","Mahalanobis" and "Mahalanobis benign".

  • pgd_l2(model, X, y, config) returns delta perturbations to be added to training data during adversarial training. l2 PGD algorithm is applied according to the given configurations which contain omega matrix of previously selected perturbation_constraint.

  • get_trained_model returns adversarially trained model with l2 PGD algorithm and prints test performance on clean data and adversarial examples

Malware_demo.ipynb

Contains a demo of adversarial training a model with specified perturbation constraints. Clean accuracy on test set and defense success on adversarial examples are tested.

nonuniform_omega

Contains necessary data for Omega matrix calculation such as Pearson correlation coefficients, Shapley values, covariance matrix of the training set to calculate Mahalanobis distance and covariance matrix of only benign class for Mahalanobis benign.

Adversarial_example_sets

Contains the adversarial example sets generated by the winner attacks of malware evasion competition 2019.

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Comments
  • Bump numpy from 1.19.4 to 1.22.0

    Bump numpy from 1.19.4 to 1.22.0

    Bumps numpy from 1.19.4 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

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    ... (truncated)

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  • Where are the other attack samples?

    Where are the other attack samples?

    https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations/blob/575f93e82d0a784290cf6b869e2dfee5171f689d/NonuniformRobustness.py#L56 Here we can see you have other attack samples, but not in https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations/tree/main/Adversarial_example_sets Could you upload those sample too, please?

    opened by Zhanyu-Wang 2
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