2021-malware-detection-classification

Malware detection is an important process in modern computing to help protect various systems from getting infected. The goal for any project, program, or system that aims to detect malware is to prevent any malicious software from running on a user’s computer. With our project, we have aimed to assist in the battle against malicious software by creating a model that can detect and label different types of programs as either malware or benign software. For this project, we used a Deep Neural Network (DNN) model. archi The architecture of our model, shown above, consists of a dense layer with relu activation, a batch normalization layer, and finally a dropout layer. As shown in the diagram, we use 10 of these layers. This project takes inspiration from the paper “Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability” created by Benjamin Marais, Tony Quertier, and Christophe Chesneau.

amanthanvi

Config files for my GitHub profile.

armstrong-number-calculator

A program that calculates if a number is an Armstrong number

bugbountyscanner

A Bash script and Docker image for Bug Bounty reconnaissance. Intended for headless use.

cmsc389t-personal-website

Aman Thanvi Project 4 - Personal Website for CMSC389T WInter 2023

docker-cargo-fuzz

docker image of rust nightly with cargo-fuzz installed

gapur

Hi there 🖖, This is my Github README.

malicious-url-detector

mayhem-cmake-example

nfa-to-dfa

a visual NFA to DFA converter

personal-website

sv-parser

SystemVerilog parser library fully compliant with IEEE 1800-2017

todo-app

unit-converter