Malware-Detection-Using-Machine-Learning

About The Project

• This project analyzes PE information of exe files to detect malware.
• In this repository you will learn how to create your own dataset and will be able to see the use of machine learning models using the dataset.
• We will use machine learning for detect malware.

Getting Started

This pe information was extracted using pefile.

If you want to create your own dataset, check out Data_Set_Generator.ipynb

or you can use data-set/MalwareDataSet.csv

Dataset

This dataset (MalwareDataSet.csv) contains a total of 137,444 data. There are 96,526 safe and 40,918 malware.

It has a total of 9 columns. The information of these columns is as follows:

* AddressOfEntryPoint
* MajorLinkerVersion
* MajorImageVersion
* MajorOperatingSystemVersion
* DllCharacteristics
* SizeOfStackReserve
* NumberOfSections
* ResourceSize
* legitimate

Classification

We used 3 different classification algorithms. These :

* K Nearest Neighbors (KNN)
* Decision Tree
* Random Forest

Results

The Success Rate was calculated as % 97.56118855679404 with the K-Nearest-Neighbors

The Success Rate was calculated as % 98.59433660254359 with the Decision Tree

The Success Rate was calculated as % 99.1240068682518 with the Random Forest