syssec-laboratory Goto Github PK

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Name: System Security Lab

Type: User

System Security Lab's Projects

0day-security-software-vulnerability-analysis-technology

0day安全_软件漏洞分析技术

adversarialnetspapers

The classical paper list with code about generative adversarial nets

awesome

:computer: 🎉 An awesome & curated list of best applications and tools for Windows.

awesome-computerscience

An awesome & curated list of computer science historical work.

awesome-machine-learning-on-source-code

Cool links & research papers related to Machine Learning applied to source code (MLonCode)

awesome-ml-for-cybersecurity

:octocat: Machine Learning for Cyber Security

OWASP Benchmark is a test suite designed to verify the speed and accuracy of software vulnerability detection tools. A fully runnable web app written in Java, it supports analysis by Static (SAST), Dynamic (DAST), and Runtime (IAST) tools that support Java. The idea is that since it is fully runnable and all the vulnerabilities are actually exploitable, it’s a fair test for any kind of vulnerability detection tool. For more details on this project, please see the OWASP Benchmark Project home page.

c2ocaml

c2ocaml - a source-to-source transformer to take C files into OCaml files compatible with lsee (a lightweight symbolic execution engine)

ccsc-editor

centaur

Symbolic Execution of Android Framework

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

chucky-ng

Projects based on joern

code-imitator

code2vec

MLonCode community effort to implement Learning Distributed Representations of Code (https://arxiv.org/pdf/1803.09473.pdf)

code2vec-1

TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"

codesensor

ANTLR 3 fuzzy parser

comparative_study

confine

This tool set can generate SECCOMP profiles for Docker images. It mainly relies on static analysis, making its results more reliable than currently available tools.