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Zero-Day Attack Detection Machine Learning Open Source

Advanced zero-day attack detection system using machine learning to identify previously unknown security threats through behavioral analysis and anomaly detection. Implements unsupervised learning, Isolation Forest, Autoencoder models, real-time threat detection, and adaptive learning capabilities.

Behavioral Analysis Anomaly Detection Real-time Detection Isolation Forest Download Now Jupyter Notebook Autoencoder Get Started
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Zero-Day Attack Detection ML Project - RSK World
Zero-Day Attack Detection ML Project - RSK World
Machine Learning Zero-Day Detection Python Behavioral Analysis Cybersecurity Anomaly Detection

This project implements an advanced Zero-Day Attack Detection System using machine learning to identify previously unknown security threats through behavioral analysis and anomaly detection. It uses unsupervised learning with Isolation Forest and Autoencoder models, extracts advanced features from system and network data, and provides real-time threat detection capabilities. The system includes adaptive learning, comprehensive visualization, alert systems, and batch processing for continuous monitoring and detection of novel security threats.

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Behavioral Analysis

Analyzes system behavior patterns to identify anomalies and detect previously unknown attacks through behavioral pattern analysis.

  • System behavior tracking
  • Pattern anomaly detection
  • Baseline establishment
  • Behavior sequence analysis

Unsupervised Anomaly Detection

Uses Isolation Forest and Autoencoder models for unsupervised learning to detect zero-day attacks without prior knowledge.

  • Isolation Forest algorithm
  • Autoencoder deep learning
  • Ensemble approach
  • Reconstruction error detection

Advanced Feature Engineering

Extract comprehensive features from system and network data including statistical, temporal, and behavioral features.

  • Statistical feature extraction
  • Temporal feature analysis
  • Interaction features
  • Behavioral feature computation

Real-time Threat Detection

Continuous monitoring and detection capabilities with multi-model ensemble predictions and threat level classification.

  • Continuous monitoring
  • Ensemble predictions
  • Threat level classification
  • Real-time statistics

Adaptive Learning System

Self-improving detection system with performance-based threshold adaptation and feedback-based learning.

  • Self-improving detection
  • Threshold adaptation
  • Model weight updates
  • Feedback-based learning

Comprehensive Visualization

Generate visual reports, dashboards, and threat analysis charts including timeline visualization and model comparison plots.

  • Threat timeline visualization
  • Model comparison plots
  • Feature importance charts
  • Automated report generation

Alert System

Configurable alert system with email notifications, file logging, and console alerts for detected threats.

  • Email alerts (SMTP)
  • File logging
  • Console notifications
  • Configurable thresholds

Configuration Management

YAML/JSON configuration files for easy customization of model parameters, thresholds, and system settings.

  • JSON/YAML support
  • Hierarchical configuration
  • Default value management
  • Easy customization

Model Evaluation

Comprehensive metrics including ROC curves, PR curves, F1 score, precision, recall, and confusion matrix analysis.

  • ROC and PR curves
  • Comprehensive metrics
  • Model comparison
  • Optimal threshold finding

Batch Processing

Process multiple files in parallel or sequentially with detailed CSV exports and batch results summary.

  • Parallel processing
  • Batch results summary
  • CSV export
  • Error handling per file

Feature Importance Analysis

Identify most important features using permutation importance, correlation-based, and variance-based methods.

  • Permutation importance
  • Correlation analysis
  • Variance-based importance
  • Ensemble importance

Threshold Optimization

Auto-optimize detection thresholds using F1 score optimization, precision-recall balance, and Youden's J statistic.

  • F1 score optimization
  • Precision-recall balance
  • Youden's J statistic
  • Multiple optimization methods

Data Validation

Validate input data format and quality before processing with comprehensive validation reports.

  • Data structure validation
  • Numeric data validation
  • Quality assessment
  • Missing value analysis

Jupyter Notebooks

Interactive Jupyter Notebooks for data exploration, model training, evaluation, and visualization.

  • Interactive analysis
  • Model training workflows
  • Evaluation notebooks
  • Visualization examples

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • NumPy 1.21.0+
  • Pandas 1.3.0+
  • Scikit-learn 1.0.0+
  • TensorFlow 2.8.0+
  • Matplotlib 3.4.0+
  • Seaborn 0.11.0+
  • Jupyter 1.0.0+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • Scikit-learn - BSD License
  • TensorFlow - Apache 2.0 License
  • Pandas - BSD License
  • NumPy - BSD License
  • Matplotlib - PSF License
  • Seaborn - BSD License
  • Jupyter - BSD License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • Zero-Day Detection ML Documentation
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Get the complete source code for this zero-day attack detection project. You can view the code or download the source code directly.

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Categories

Machine Learning Zero-Day Detection Python Behavioral Analysis Cybersecurity Anomaly Detection

Technologies

Python 3.8+
Scikit-learn
TensorFlow
Pandas
NumPy
Matplotlib

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Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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