Malware Detection Using ML/DL

Detect any URL is malicious or not or any malware

Project Description 

A machine learning–based cybersecurity system designed to detect malicious URLs and classify malware files. The system combines traditional machine learning and deep learning techniques to identify potential cyber threats. Logistic Regression is used to analyze URL patterns and detect malicious links, while a TensorFlow-based deep learning model classifies files as malware or safe. The solution is deployed using a Flask web application that allows users to submit URLs or files for real-time threat detection. 

Features 

• Malicious URL detection using Logistic Regression.

• Malware file classification using a TensorFlow deep learning model.

• Real-time threat detection through a web interface.

• Integration of machine learning and deep learning models in a single system.

• User-friendly web application built with Flask.

• Automated prediction and classification of cybersecurity threats.

Tech Stack

• Programming Language: Python

• Machine Learning: Scikit-learn (Logistic Regression)

• Deep Learning: TensorFlow, ANN

• Web Framework: Flask

• Libraries: NumPy, Pandas, Matplotlib, Scikit-learn

System Requirements

• Python 3.10+

• Anaconda

• No GPU is required

Deliverables 

• Jupyter Notebook

• Dataset

• Setup over video call 

• WhatsApp support