RSK World - Botnet Detection System | Machine Learning | Network Security | Cybersecurity | RSK World

Machine Learning Botnet Detection Python Network Security Cybersecurity DNS Analysis

This project implements an advanced Botnet Detection System using machine learning algorithms to identify botnet-infected devices and compromised systems in network traffic. It employs multiple ML algorithms including Random Forest, Gradient Boosting, and SVM for accurate botnet classification. The system analyzes network communication patterns, DNS queries, traffic characteristics, and behavioral indicators to detect botnet activities with high accuracy and provides real-time detection with comprehensive reporting.

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Network Traffic Analysis

Analyze network communication patterns, traffic characteristics, and behavioral indicators to identify botnet-infected devices.

Network traffic pattern analysis
Statistical traffic metrics
Connection pattern detection
Traffic flow analysis

Botnet Classification

Classify network traffic and devices as botnet-infected or legitimate using machine learning models with high accuracy and confidence scoring.

Real-time botnet detection
Probability scoring
Risk level classification
Threshold optimization

Multiple ML Algorithms

Compare and use multiple machine learning models including Random Forest, Gradient Boosting, SVM, and Logistic Regression with ensemble support.

Random Forest classifier
Gradient Boosting models
SVM classifier
Logistic Regression

DNS Query Analysis

Analyze DNS queries to detect suspicious patterns, query rates, and identify compromised devices through DNS behavior analysis.

DNS query rate calculation
Suspicious DNS pattern detection
Query frequency analysis
DNS-based botnet detection

Jupyter Notebooks

Interactive Jupyter Notebooks for network traffic analysis, feature extraction, model training, and evaluation.

Data exploration notebook
Feature extraction notebook
Model training notebook
Evaluation and visualization

Advanced Feature Extraction

Comprehensive feature extraction including network metrics, DNS patterns, traffic statistics, and botnet indicators.

Network metric extraction
Traffic statistics
DNS pattern analysis
Botnet pattern detection

Pattern Recognition

Identify botnet patterns in network traffic using machine learning models with feature importance analysis and pattern visualization.

Botnet pattern detection
Feature importance
Traffic pattern analysis
Visualization tools

Hyperparameter Tuning

Optimize model performance with GridSearchCV and RandomizedSearchCV for better botnet detection accuracy.

GridSearchCV optimization
RandomizedSearchCV
Automated tuning
Model optimization

Cross-Validation

Robust model evaluation with Stratified K-Fold cross-validation and automated model selection.

Stratified K-Fold CV
Model comparison
Model selection
Performance metrics

Model Evaluation & Comparison

Comprehensive evaluation with multiple metrics, cross-validation, and side-by-side model comparison.

Accuracy, Precision, Recall
F1-Score and ROC AUC
Confusion matrix
Model comparison

Batch Network Traffic Processing

Process multiple network traffic records efficiently with batch detection, export capabilities, and comprehensive analysis.

Batch processing
CSV/JSON export
Multiple input formats
Comprehensive reports

REST API Server

Full RESTful API for real-time botnet detection, batch processing, and integration with other systems.

Real-time detection API
Batch prediction API
Health check endpoint
System integration support

Visualization Dashboard

Interactive visualization dashboard with data overview, feature analysis, traffic patterns, and automatic export.

Data overview charts
Feature distribution plots
Traffic pattern visualization
High-resolution export

Model Versioning

Manage and compare multiple model versions with metadata tracking and performance comparison.

Version tracking
Metadata management
Version comparison
Active version management

Feature Selection

Multiple feature selection methods including K-Best, RFE, Mutual Information, and model-based selection.

K-Best selection
Recursive Feature Elimination
Mutual Information selection
Model-based selection

Requirements

The following are the technical requirements for this project:

Python 3.8+
Scikit-learn 1.3.0+
Pandas 2.0.0+
NumPy 1.24.0+
Matplotlib 3.7.0+
Seaborn 0.12.0+
Flask 2.3.0+
Jupyter Notebook 1.0.0+

Credits & Acknowledgments

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

Python - PSF License
Scikit-learn - BSD License
NumPy - BSD License
Pandas - 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
Botnet Detection ML Documentation

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Technologies

Python 3.8+

Scikit-learn

Flask

NumPy

Pandas

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ML-based system to detect botnet activities and compromised devices in network t...

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Botnet Detection System Machine Learning Open Source

Network Traffic Analysis

Botnet Classification

Multiple ML Algorithms

DNS Query Analysis

Jupyter Notebooks

Advanced Feature Extraction

Pattern Recognition

Hyperparameter Tuning

Cross-Validation

Model Evaluation & Comparison

Batch Network Traffic Processing

REST API Server

Visualization Dashboard

Model Versioning

Feature Selection

Requirements

Credits & Acknowledgments

Support & Contact

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Download Free Source Code

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