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Rich Moulton's Projects

coursera-algorithmic-toolbox icon coursera-algorithmic-toolbox

This repository contains all the solutions for the assignments of the course - Algorithmic Toolbox offered on Coursera.

csc-748-software-exploitation-spring-2020 icon csc-748-software-exploitation-spring-2020

This course is designed to expose students to advanced exploitation techniques. Topics include the use of automated exploitation tools as well as the process of exploitation discovery and development. Vulnerability analysis, debugging, fuzzing, shellcode, and mitigation techniques will be explored. Both Windows and Linux platforms will be covered.

cve-2014-7920-7921 icon cve-2014-7920-7921

Exploit code for CVE-2014-7920 and CVE-2014-7921 - code-exec in mediaserver up to Android 5.1

danderspritz_docs icon danderspritz_docs

The goal of this project is to examine, reverse, and document the different modules available in the Equation Group's DanderSpritz post-exploitation framework leaked by the ShadowBrokers

debug-recipes icon debug-recipes

My notes collected while debugging various .NET and Windows problems.

detours icon detours

Detours is a software package for monitoring and instrumenting API calls on Windows. It is distributed in source code form.

dnspy icon dnspy

.NET assembly editor, decompiler, and debugger

docker101 icon docker101

Resources to present an introduction to Docker presentation or workshop

dotfiles icon dotfiles

Development configuration files for Startup Engineering MOOC. Intended to be forked and customized by students.

easyhook icon easyhook

EasyHook - The reinvention of Windows API Hooking

fibratus icon fibratus

Tool for exploration and tracing of the Windows kernel

fuzzing icon fuzzing

Tutorials, examples, discussions, research proposals, and other resources related to fuzzing

gabohren icon gabohren

Visual and auditory training using Gabor patches

giraffe icon giraffe

Gracefully Integrated Remote Access For Files and Execution

handson-ml icon handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

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