Explore @seclab-ucr Open Source projects

UCR Security Lab (@seclab-ucr)

seclab-ucr

Stars
3,863
Global Org. Rank 5,700 (Top 2 %)
Registered about 7 years ago
Most used languages

C++
40.9 %

Python
27.3 %

C
13.6 %

Go
9.1 %

JavaScript
4.5 %

HTML
4.5 %
Location 🇺🇸 United States
Country Total Rank 2,855
Country Ranking

C
124

C++
1,266

Go
3,071

Python
3,571

JavaScript
8,106

INTANG

SymTCP

Automatic Discrepancy Discovery for DPI Elusion

tcp_exploit

Off-Path TCP Exploit: How Wireless Routers Can Jeopardize Your Secret

KOOBE

Towards Facilitating Exploit Generation of Kernel Out-Of-Bounds Write Vulnerabilities

SUTURE

Precise and high-order static points-to/taint analysis based on LLVM IR.

SADDNS

SADDNS: Side Channel Based DNS Cache Poisoning Attack

SyzDescribe

LLift

The source code of project "LLift" (Enhancing static analysis with LLM)

SyzGen_setup

UBITect

IncreLux

Progressive Scrutiny: Incremental Detection of UBI bugs in the Linux Kernel

GPT-Expr

Assisting Static Analysis with Large Language Models: A ChatGPT Experiment

SyzBridge

SyzBridge is a research project that adapts Linux upstream PoCs to downstream distributions. It provides rich interfaces that allow you to do a lot of cool things with Syzbot bugs

Themis

Themis: Ambiguity-Aware Network Intrusion Detection based on Symbolic Model Comparison

ShadowBlock

Code release for our WWW 2019 paper entitled "ShadowBlock: A Lightweight and Stealthy Adblocking Browser".

Unias

A Hybrid Alias Analysis

K-LEAK

SADDNS2.0

CLAP

This repository hosts the implementation of CLAP (Context Learning-based Adversarial Protection) that is proposed in our CoNEXT 2020 paper titled "You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning". The code here can be used to reproduce the main results in the paper.

SyzGenPlusPlus

SCENT

TCP Side Channel Excavation Tool

CCS24Mesh

Patchlocator

An Investigation of the Android Kernel Patch Ecosystem Usenix security 21

PAPP

Prefetcher-Aware Prime+Probe

A4

Code and dataset release for our ACSAC 2021 paper titled "Eluding ML-based Adblockers With Actionable Adversarial Examples".