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Academic papers related to fuzzing, binary analysis, and exploit dev, which I want to read or have already read

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papers-we-love

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Papers from the computer science community to read and discuss.

commaai's avatar

research

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dataset and code for 2016 paper "Learning a Driving Simulator"

Quick Overview

The 0xricksanchez/paper_collection repository is a curated collection of academic papers and research articles related to various aspects of computer security, with a focus on fuzzing, vulnerability discovery, and exploit development. This repository serves as a valuable resource for researchers, security professionals, and enthusiasts interested in staying up-to-date with the latest advancements in the field.

Pros

  • Comprehensive collection of papers covering a wide range of security topics
  • Well-organized structure with papers categorized by subject
  • Regular updates with new papers added frequently
  • Includes both seminal works and cutting-edge research

Cons

  • Lacks detailed summaries or reviews of the papers
  • No search functionality within the repository
  • Some categories may have limited papers compared to others
  • Potential copyright concerns for direct hosting of academic papers

Getting Started

As this is not a code library but a collection of academic papers, there is no code example or quick start section. To use this repository:

  1. Visit the GitHub repository: https://github.com/0xricksanchez/paper_collection
  2. Browse through the different categories to find papers of interest
  3. Click on the paper titles to view or download the PDF files
  4. Consider starring or watching the repository to stay updated with new additions

Competitor Comparisons

papers-we-love logo

papers-we-love

86,719

Papers from the computer science community to read and discuss.

Pros of papers-we-love

  • Larger community with more contributors and a wider range of topics
  • Better organized with categorized papers and a more structured repository
  • Includes meetup information and community guidelines

Cons of papers-we-love

  • May be overwhelming for newcomers due to the large number of papers
  • Less focused on specific areas compared to paper_collection
  • Requires more navigation to find papers on specific topics

Code comparison

paper_collection:

└── papers
    ├── binary_analysis
    ├── fuzzing
    ├── program_analysis
    └── reverse_engineering

papers-we-love:

├── artificial_intelligence
├── computer_graphics
├── computer_science
├── cryptography
├── data_structures
└── ...

The paper_collection repository has a more focused structure with fewer top-level categories, while papers-we-love has a broader range of topics with more extensive categorization.

Both repositories serve as valuable resources for computer science papers, but they cater to different audiences. paper_collection is more specialized, focusing on areas like binary analysis and reverse engineering, making it ideal for researchers in those fields. papers-we-love offers a broader scope, covering various computer science topics, which makes it suitable for a wider audience but may require more effort to find specific papers.

commaai logo

research

4,121

dataset and code for 2016 paper "Learning a Driving Simulator"

Pros of research

  • More active development with recent commits and updates
  • Broader scope covering various autonomous driving topics
  • Includes code implementations and practical examples

Cons of research

  • Less organized structure for paper references
  • Lacks a comprehensive list of academic publications
  • May be more challenging for newcomers to navigate

Code comparison

paper_collection:

No code available for comparison

research:

def radians_to_degrees(rad):
    return rad * 180 / math.pi

def degrees_to_radians(deg):
    return deg * math.pi / 180

Summary

paper_collection focuses on curating a collection of academic papers related to security research, while research provides a more hands-on approach to autonomous driving research with code implementations. paper_collection offers a well-organized list of publications, making it easier for researchers to find relevant papers. On the other hand, research provides practical examples and code snippets, which can be valuable for developers and engineers working on autonomous driving projects. The choice between the two repositories depends on whether the user is looking for academic references or practical implementations in the field of autonomous driving and related technologies.

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Note

The sole purpose of this repository is to help me organize recent academic papers related to fuzzing, binary analysis, IoT security, and general exploitation. This is a non-exhausting list, even though I'll try to keep it updated... Feel free to suggest decent papers via a PR.

Table of Contents

Read & Tagged

Unread

Unread papers categorized by a common main theme.

General fuzzing implementations

Harnessing

AI/LLM

IoT fuzzing

Firmware Emulation

Network fuzzing

Kernel fuzzing

Format specific fuzzing

Exploitation

Static Binary Analysis

Misc

Surveys, SoKs, and Studies