FAVD: Featherweight Assisted Vulnerability Discovery

Related tags

Deep LearningFAVD
Overview

source under MIT license data under CC BY 4.0 license DOI

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:

  1. gathering of the labeled function names that are used as the source for step 2, in names
  2. dangerous word identification, in dangerous-words
  3. 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).

You might also like...
A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery
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

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

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

SegSwap Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery" [PDF] [Project page] If our project

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

From "Onion Not Found" to Guard Discovery (PETS'22) This repository holds the code and data for our PETS'22 paper titled 'From "Onion Not Found" to Gu

Systemic Evolutionary Chemical Space Exploration for Drug Discovery
Systemic Evolutionary Chemical Space Exploration for Drug Discovery

SECSE SECSE: Systemic Evolutionary Chemical Space Explorer Chemical space exploration is a major task of the hit-finding process during the pursuit of

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

WASP2 (Currently in pre-development): Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis Requ

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

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

NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen: activation optimized image synthesis for discovery neuroscience

NeuroGen: activation optimized image synthesis for discovery neuroscience NeuroGen is a framework for synthesizing images that control brain activatio

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

The GT4SD (Generative Toolkit for Scientific Discovery) is an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-the-art generative AI models easier to use.

Comments
  • Bump numpy from 1.21 to 1.22.0 in /dangerous-words

    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

    Dependabot compatibility score

    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
Releases(v1.0.0)
Owner
secureIT
IKTLUSS funded research project on automated detection of software security vulnerabilities
secureIT
Özlem Taşkın 0 Feb 23, 2022
A collection of resources, problems, explanations and concepts that are/were important during my Data Science journey

Data Science Gurukul List of resources, interview questions, concepts I use for my Data Science work. Topics: Basics of Programming with Python + Unde

Smaranjit Ghose 10 Oct 25, 2022
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
COVID-Net Open Source Initiative

The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available

Linda Wang 1.1k Dec 26, 2022
MILK: Machine Learning Toolkit

MILK: MACHINE LEARNING TOOLKIT Machine Learning in Python Milk is a machine learning toolkit in Python. Its focus is on supervised classification with

Luis Pedro Coelho 610 Dec 14, 2022
Solution to the Weather4cast 2021 challenge

This code was used for the entry by the team "antfugue" for the Weather4cast 2021 Challenge. Below, you can find the instructions for generating predi

Jussi Leinonen 13 Jan 03, 2023
Kinetics-Data-Preprocessing

Kinetics-Data-Preprocessing Kinetics-400 and Kinetics-600 are common video recognition datasets used by popular video understanding projects like Slow

Kaihua Tang 7 Oct 27, 2022
Code for "AutoMTL: A Programming Framework for Automated Multi-Task Learning"

AutoMTL: A Programming Framework for Automated Multi-Task Learning This is the website for our paper "AutoMTL: A Programming Framework for Automated M

Ivy Zhang 40 Dec 04, 2022
[CVPR 2021] Monocular depth estimation using wavelets for efficiency

Single Image Depth Prediction with Wavelet Decomposition Michaël Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit and Daniyar Turmukhambeto

Niantic Labs 205 Jan 02, 2023
Yolov5 + Deep Sort with PyTorch

딥소트 수정중 Yolov5 + Deep Sort with PyTorch Introduction This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of obj

1 Nov 26, 2021
The official homepage of the COCO-Stuff dataset.

The COCO-Stuff dataset Holger Caesar, Jasper Uijlings, Vittorio Ferrari Welcome to official homepage of the COCO-Stuff [1] dataset. COCO-Stuff augment

Holger Caesar 715 Dec 31, 2022
Localization Distillation for Object Detection

Localization Distillation for Object Detection This repo is based on mmDetection. This is the code for our paper: Localization Distillation

274 Dec 26, 2022
Hunt down social media accounts by username across social networks

Hunt down social media accounts by username across social networks Installation | Usage | Docker Notes | Contributing Installation # clone the repo $

1 Dec 14, 2021
AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

AsymmetricGAN for Image-to-Image Translation AsymmetricGAN Framework for Multi-Domain Image-to-Image Translation AsymmetricGAN Framework for Hand Gest

Hao Tang 42 Jan 15, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling

Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Code for the paper: Greg Ver Steeg and Aram Galstyan. "Hamiltonian Dynamics with N

Greg Ver Steeg 25 Mar 14, 2022
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper) (Accepted for oral presentation at ACM

Minha Kim 1 Nov 12, 2021
One-line your code easily but still with the fun of doing so!

One-liner-iser One-line your code easily but still with the fun of doing so! Have YOU ever wanted to write one-line Python code, but don't have the sa

5 May 04, 2022
Virtual hand gesture mouse using a webcam

NonMouse 日本語のREADMEはこちら This is an application that allows you to use your hand itself as a mouse. The program uses a web camera to recognize your han

Yuki Takeyama 55 Jan 01, 2023
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta rlmeta - a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib Installation To build fro

Meta Research 281 Dec 22, 2022