SECURE
DETECT
PROTECT
ANALYZE
MALWARE
DEFENSE

🛡️ Malware Detection System

Advanced Machine Learning-Based PE File Analysis

📋 Project Overview

The Malware Detection System is an advanced machine learning-based application designed to identify potentially malicious Portable Executable (PE) files. By analyzing the structural characteristics and header information of Windows executable files (.exe, .dll, .sys), our system can accurately distinguish between benign and malicious software with high precision.

This project leverages static analysis techniques, extracting key features from PE headers without executing the files, ensuring safe and efficient malware detection. The system is built using modern web technologies and machine learning frameworks, providing a user-friendly interface for security analysts and researchers.

✨ Key Features

  • Real-time Analysis: Instant malware detection with confidence scoring
  • PE File Support: Analyzes .exe, .dll, and .sys files
  • Static Analysis: Safe feature extraction without file execution
  • 18 PE Header Features: Comprehensive analysis of file structure
  • Confidence Scoring: Probability-based prediction with percentage confidence
  • Secure File Handling: Automatic cleanup and size limitations (25MB max)
  • Feature Visualization: Detailed view of extracted PE characteristics
  • Web-based Interface: Easy-to-use browser interface with no installation required

🔧 Technology Stack

Backend Framework

Flask 2.x

Lightweight Python web framework for building the REST API and serving the application

Machine Learning

scikit-learn

Robust ML library for model training and predictions

joblib

Efficient model serialization and persistence

PE File Analysis

pefile

Python PE file parser for extracting header information and structural features

Data Processing

pandas

Data manipulation and feature engineering

NumPy

Numerical computing support

Frontend

HTML

Modern web standards for UI

CSS

Template engine for dynamic content

Security

vs code

Secure filename handling and upload validation

🏗️ System Architecture

1. File Upload

User uploads PE file through web interface

2. Validation

File type and size validation (max 25MB)

3. Secure Storage

Temporary storage with timestamped filename

4. Feature Extraction

Extract 18 PE header features using pefile

5. ML Prediction

Pre-trained model analyzes features

6. Results Display

Show prediction, confidence, and features

7. Cleanup

Automatic removal of uploaded file

📊 Extracted PE Features

The system analyzes 18 critical features from PE file headers:

Feature Name Description Source
TimeDateStamp Compilation timestamp of the file FILE_HEADER
Characteristics File characteristics flags FILE_HEADER
MajorLinkerVersion Linker major version number OPTIONAL_HEADER
SizeOfInitializedData Size of initialized data section OPTIONAL_HEADER
AddressOfEntryPoint Entry point address for execution OPTIONAL_HEADER
ImageBase Preferred base address in memory OPTIONAL_HEADER
MajorOperatingSystemVersion Required OS major version OPTIONAL_HEADER
MinorOperatingSystemVersion Required OS minor version OPTIONAL_HEADER
MajorImageVersion Image major version OPTIONAL_HEADER
MinorImageVersion Image minor version OPTIONAL_HEADER
MajorSubsystemVersion Subsystem major version OPTIONAL_HEADER
MinorSubsystemVersion Subsystem minor version OPTIONAL_HEADER
SizeOfHeaders Combined size of all headers OPTIONAL_HEADER
CheckSum Image file checksum OPTIONAL_HEADER
Subsystem Required subsystem identifier OPTIONAL_HEADER
DllCharacteristics DLL characteristics flags OPTIONAL_HEADER
SizeOfStackReserve Stack reservation size OPTIONAL_HEADER
ImageDirectoryEntryExport Export directory size DATA_DIRECTORY

🤖 Machine Learning Model

Model Training Process

The ML model is trained on a comprehensive dataset of known malware and benign PE files. The training process involves:

  1. Feature extraction from labeled PE file dataset
  2. Data preprocessing and normalization
  3. Model selection and hyperparameter tuning
  4. Cross-validation for performance evaluation
  5. Model serialization using joblib

Model Performance Metrics

~98%

Accuracy

~99.1%

Precision

~99%

Recall

~98.2

F1-Score

Note: Actual performance metrics depend on the specific dataset and model used during training.

⚙️ Installation & Setup

Prerequisites

  • Python 3.7 or higher
  • pip package manager
  • Virtual environment (recommended)

Installation Steps

# 1. Clone the repository
git clone https://github.com/yourusername/malware-detection-system.git
cd malware-detection-system

# 2. Create virtual environment
python -m venv venv

# 3. Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# 4. Install dependencies
pip install flask pandas scikit-learn joblib pefile werkzeug

# 5. Train the model (if not already trained)
python train_model.py

# 6. Run the application
python app.py

# 7. Access the application
# Open browser and navigate to: http://127.0.0.1:5000

🔍 How It Works

  1. Upload: Select a PE file (.exe, .dll, or .sys) from your computer
  2. Validation: System checks file type and size (max 25MB)
  3. Processing: File is temporarily saved with a unique timestamp
  4. Analysis: PE headers are parsed to extract 18 key features
  5. Prediction: Machine learning model analyzes features
  6. Results: Display prediction (Malware/Benign) with confidence percentage
  7. Cleanup: Uploaded file is automatically deleted for security

🔐 Security Considerations

Security Features Implemented:

  • File size limitation (25MB max) to prevent DoS attacks
  • Secure filename handling using Werkzeug's secure_filename()
  • Timestamp-based unique filenames to prevent collisions
  • Automatic file cleanup after processing
  • Static analysis only - no file execution
  • Restricted file types (.exe, .dll, .sys only)
  • Error handling with graceful fallbacks
⚠️ Important: This system is for educational and research purposes. Always handle potentially malicious files in isolated environments.

🔌 API Endpoints

Endpoint Method Description
/ GET, POST Main interface for file upload and analysis
/about GET Information about the system
/project GET Detailed project documentation

🚀 Future Enhancements

  • Support for additional file formats (APK, PDF, Office documents)
  • Dynamic analysis integration using sandbox environments
  • Behavioral analysis and heuristic detection
  • Real-time threat intelligence integration
  • Batch file processing capabilities
  • API rate limiting and authentication
  • Advanced visualization of malware characteristics
  • Machine learning model versioning and A/B testing
  • Integration with VirusTotal API for cross-validation
  • Detailed analysis reports in PDF format
  • User account system with analysis history
  • REST API for programmatic access

⚠️ Known Limitations

  • Limited to Windows PE files only
  • Static analysis may miss runtime behaviors
  • Obfuscated or packed malware may evade detection
  • Model accuracy depends on training data quality
  • No support for encrypted or password-protected files
  • Maximum file size limited to 25MB

👥 Contributors & Acknowledgments

This project was developed as part of a machine learning and cybersecurity initiative. Special thanks to the open-source community for providing the tools and libraries that made this project possible.

  • Flask community for the web framework
  • scikit-learn developers for ML tools
  • pefile maintainers for PE parsing capabilities
  • Security researchers for malware datasets

📄 License

This project is released under the MIT License. Feel free to use, modify, and distribute the code for educational and research purposes. Commercial use should be done with proper attribution and consideration of security implications.

📧 Contact & Support

For questions, bug reports, or feature requests, please open an issue on the GitHub repository or contact the development team.

GitHub Repository Contact Developer