Giters

System-CTL / Machine-Learning-Based-Malware-Scanner

Malware Scanner based on the binary classification as the dataset is classify into two classes (Clean ,Malware) exe data . The dataset contain portable excutable(PE) header features which have malicious and clean features values in CSV file. The scanner declare the executable as malicious on the basis of its some malicious header features . After trained the model we deployed the ML model on the WEB APP and DESKTOP APP which have multiple scanning options

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Machine-Learning-Based-Malware-Scanner

Malware Scanner used binary classification strategy for that the dataset is classify into two classes (Clean ,Malware) exe data . The dataset contain portable excutable(PE) header features which have malicious and clean features values in CSV file. The scanner declare the executable as malicious on the basis of multiple PE malicous features. After that trained model saved in pickle file which run against executable file. This project deployed on WEB APP and DESKTOP APP which have multiple scanning options.

NOTE

I have wrote paper on this work so, when it get published then i will upload the source code and all the documentation of this project.

About

Malware Scanner based on the binary classification as the dataset is classify into two classes (Clean ,Malware) exe data . The dataset contain portable excutable(PE) header features which have malicious and clean features values in CSV file. The scanner declare the executable as malicious on the basis of its some malicious header features . After trained the model we deployed the ML model on the WEB APP and DESKTOP APP which have multiple scanning options