RSK World - DDoS Detection | Machine Learning | Network Security | Real-time Monitoring | RSK World

Machine Learning DDoS Detection Python Network Security TensorFlow Cybersecurity

This project implements a DDoS Attack Detection System using machine learning and network traffic analysis techniques. It employs multiple ML algorithms including Random Forest, SVM, Neural Networks, and Ensemble methods for attack classification. The system analyzes network traffic patterns, packet rates, connection characteristics, and flow features to detect and classify DDoS attacks including SYN flood, UDP flood, HTTP flood, and ICMP flood with high accuracy.

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Real-time Traffic Monitoring

Monitor network traffic in real-time to detect DDoS attacks as they occur. Analyze packet flows, connection rates, and network patterns.

Live packet capture and analysis
Network interface monitoring
Traffic flow analysis
Real-time attack detection

Attack Type Classification

Detect and classify multiple types of DDoS attacks including SYN flood, UDP flood, HTTP flood, and ICMP flood attacks.

SYN flood detection
UDP flood identification
HTTP flood analysis
ICMP flood detection

Multiple ML Algorithms

Compare and use multiple machine learning models including Random Forest, SVM, Neural Networks, and Ensemble methods.

Random Forest classifier
Support Vector Machine
Neural Network models
Ensemble voting classifier

Attack Severity Scoring

Automatic severity calculation (0-100) with CRITICAL/HIGH/MEDIUM/LOW levels based on attack characteristics.

Severity score calculation
Attack level classification
Confidence-based scoring
Risk assessment

Jupyter Notebooks

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

Data exploration notebook
Feature engineering notebook
Model training notebook
Evaluation and analysis

Feature Extraction

Comprehensive feature extraction from network packets including packet features, flow features, and statistical features.

Packet-level features
Flow-based features
Statistical analysis
Time-window features

Attack Pattern Detection

Identify recurring, escalating, and coordinated attacks with pattern recognition and analysis.

Recurring attack detection
Escalating pattern analysis
Coordinated attack identification
Attack wave detection

Attack Prediction

Early warning system predicting attack probability based on current traffic patterns and historical data.

Attack probability prediction
Early warning alerts
Pattern-based forecasting
Proactive defense

IP Reputation System

Track and score IP addresses based on attack history and suspicious activity patterns.

IP reputation scoring
Attack history tracking
Suspicious IP identification
Reputation-based filtering

Mitigation Recommendations

AI-powered automated mitigation suggestions and ready-to-use commands for each attack type.

Automated recommendations
Attack-specific mitigation
Quick action commands
Best practice suggestions

Multi-Format Reports

Generate comprehensive reports in CSV, JSON, and HTML formats with detailed attack statistics and analysis.

CSV report generation
JSON data export
HTML visual reports
Statistical analysis

REST API Server

Full RESTful API for integration with other systems, monitoring tools, and security platforms.

RESTful API endpoints
Real-time detection API
Statistics and analytics API
System integration support

Web Dashboard

Interactive web dashboard for visualization, monitoring, and management of DDoS detection system.

Real-time visualization
Attack statistics dashboard
Performance metrics
Interactive charts and graphs

Attack Simulator

Test your system with simulated DDoS attacks of various types and intensities.

SYN flood simulation
UDP flood testing
HTTP flood simulation
Configurable intensity

Network Packet Analysis

Advanced packet analysis using Scapy for deep inspection of network traffic and protocols.

Packet capture and analysis
Protocol inspection
Traffic flow tracking
Deep packet inspection

Requirements

The following are the technical requirements for this project:

Python 3.8+
Scikit-learn 1.0.0+
TensorFlow 2.8.0+
Pandas 1.3.0+
Scapy 2.4.5+
Flask 2.0.0+
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
TensorFlow - Apache 2.0 License
Scapy - GPLv2 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
DDoS Detection Documentation

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Download Free Source Code

Get the complete source code for this project. You can view the code or download the source code directly.

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Technologies

Python 3.8+

Scikit-learn

TensorFlow

Pandas

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DDoS Attack Detection Machine Learning Open Source

Real-time Traffic Monitoring

Attack Type Classification

Multiple ML Algorithms

Attack Severity Scoring

Jupyter Notebooks

Feature Extraction

Attack Pattern Detection

Attack Prediction

IP Reputation System

Mitigation Recommendations

Multi-Format Reports

REST API Server

Web Dashboard

Attack Simulator

Network Packet Analysis

Requirements

Credits & Acknowledgments

Support & Contact

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Download Free Source Code

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