FAVD: Featherweight Assisted Vulnerability Discovery

This repository contains the replication package for the paper "Featherweight Assisted Vulnerability Discovery", David Binkley, Leon Moonen, Sibren Isaacman, Information and Software Technology, 2022, 106844, ISSN 0950-5849, DOI: 10.1016/j.infsof.2022.106844. https://www.sciencedirect.com/science/article/pii/S0950584922000209.

The replication package is archived on Zenodo with DOI: 10.5281/zenodo.5957264. The source code is distributed under the MIT license, the data is distributed under the CC BY 4.0 license.

Repository Organization

The overall process consists of three steps, organized as three directories:

gathering of the labeled function names that are used as the source for step 2, in names
dangerous word identification, in dangerous-words
analysis of the data gathered during step 2, in analysis

The directory Model holds a copy of the pre-trained LAVDNN model as provided by the authors at https://github.com/StablelJay/LAVDNN/raw/master/Model/model_of_LAVDNN

Requirements

The following tools are required for the replication:

python >= 3.5
R
tcsh
csvcut from csvkit
cntk as keras backend for running the LAVDNN model

In addition, the following python packages are needed

Finally, for the analysis in step 3, the following R libraries are needed:

agricolae, ggplot2, reshape2, xtable

Citation

If you build on this data or code, please cite this work by referring to the paper:

@article{binkley2022:featherweight,
   title = {Featherweight assisted vulnerability discovery},
   author = {David Binkley and Leon Moonen and Sibren Isaacman},
   journal = {Information and Software Technology},
   pages = {106844},
   year = {2022},
   issn = {0950-5849},
   doi = {https://doi.org/10.1016/j.infsof.2022.106844},
   url = {https://www.sciencedirect.com/science/article/pii/S0950584922000209},
   copyright = {Open Access},
   publisher = {Elsevier},
}

Acknowledgement

Part of this work has been financially supported by the Research Council of Norway through the secureIT project (RCN contract #288787).

Bumps numpy from 1.21 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

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release
fd66547 REL: Prepare for the NumPy 1.22.0 release.
125304b wip
c283859 Merge pull request #20682 from charris/backport-20416
5399c03 Merge pull request #20681 from charris/backport-20954
f9c45f8 Merge pull request #20680 from charris/backport-20663
794b36f Update armccompiler.py
d93b14e Update test_public_api.py
7662c07 Update init.py
311ab52 Update armccompiler.py
Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR
@dependabot recreate will recreate this PR, overwriting any edits that have been made to it
@dependabot merge will merge this PR after your CI passes on it
@dependabot squash and merge will squash and merge this PR after your CI passes on it
@dependabot cancel merge will cancel a previously requested merge and block automerging
@dependabot reopen will reopen this PR if it is closed
@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
@dependabot use these labels will set the current labels as the default for future PRs for this repo and language
@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

dependencies

A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

PiSL A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informe

8 Jul 13, 2022

This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

2 Sep 15, 2022

Bump numpy from 1.21 to 1.22.0 in /dangerous-words
Bumps numpy from 1.21 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

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

dependencies
opened by dependabot[bot] 0

FAVD: Featherweight Assisted Vulnerability Discovery

Related tags

Overview

FAVD: Featherweight Assisted Vulnerability Discovery

Repository Organization

Requirements

Citation

Acknowledgement

You might also like...

A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

This repository holds code and data for our PETS'22 article 'From "Onion Not Found" to Guard Discovery'.

Systemic Evolutionary Chemical Space Exploration for Drug Discovery

Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis

This respository includes implementations on Manifoldron: Direct Space Partition via Manifold Discovery

NeuroGen: activation optimized image synthesis for discovery neuroscience

GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.

Comments

Bump numpy from 1.21 to 1.22.0 in /dangerous-words

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Releases(v1.0.0)

v1.0.0(Feb 8, 2022)

Owner

secureIT

For storing the complete exploration of Visual Question Answering for our B.Tech Project

Kinetics-Data-Preprocessing

This repository contains the code for: RerrFact model for SciVer shared task

Demo code for ICCV 2021 paper "Sensor-Guided Optical Flow"

PyTorch implementation of SwAV (Swapping Assignments between Views)

A PyTorch Toolbox for Face Recognition

Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

Revealing and Protecting Labels in Distributed Training

K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

Cards Against Humanity AI

OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

Training DALL-E with volunteers from all over the Internet using hivemind and dalle-pytorch (NeurIPS 2021 demo)

PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

A PyTorch-based library for semi-supervised learning

Air Pollution Prediction System using Linear Regression and ANN

Implicit Graph Neural Networks

2D Time independent Schrodinger equation solver for arbitrary shape of well

TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio