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crackercat's Projects

lviewlol icon lviewlol

League of Legends Python based scripting platform.

lwext4 icon lwext4

ext2/ext3/ext4 filesystem library for microcontrollers

lwip icon lwip

lwIP mirror from http://git.savannah.gnu.org/cgit/lwip.git

lxbook icon lxbook

《爬虫逆向进阶实战》书籍代码库

lxgui icon lxgui

Portable, real time, modular and data-driven GUI C++ library.

lxrunoffline icon lxrunoffline

A full-featured utility for managing Windows Subsystem for Linux (WSL)

ly icon ly

display manager with console UI

lynis icon lynis

Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.

lyra icon lyra

A simple to use, composable, command line parser for C++ 11 and beyond

lyrebird-android icon lyrebird-android

本程序是一个Lyrebird的插件,用于支持获取Android设备信息。

lyscript icon lyscript

一款强大的针对x64dbg开发的Python自动化控制模块,提高逆向分析效率 。 A powerful Python automatic control module developed for x64dbg to improve the efficiency of reverse analysis

m1n1 icon m1n1

A bootloader and experimentation playground for Apple Silicon

m3u8-downloader icon m3u8-downloader

M3U8-Downloader 支持多线程、断点续传、加密视频下载缓存。

m3u8-downloader-1 icon m3u8-downloader-1

一个M3U8 视频下载(M3U8 downloader)工具。跨平台: 提供windows、linux、mac三大平台可执行文件,方便直接使用。

m3u8_dl icon m3u8_dl

m3_dl support encrpt key , support Mac/windows

m6-reveng icon m6-reveng

Info & tools for reverse engineering the M6 smart fitness band

maalik icon maalik

Feature-rich Post Exploitation Framework with Network Pivoting capabilities.

maat icon maat

Open-source symbolic execution framework: https://maat.re

mach-dump icon mach-dump

library to load and parse Mach-O core files without dependencies

mach-hook icon mach-hook

Hooking mach-o libraries in current or remote processes by patching __GOT and NLIST

machinae icon machinae

Machinae Security Intelligence Collector

machine-learning-learning-notes icon machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍,书中详细介绍了机器学习领域不同类型的算法(例如:监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等),记录了本人在学习过程中的理解思路与扩展知识点,希望对新人阅读西瓜书有所帮助!

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