RSK World - Network Anomaly Detection | Machine Learning | Unsupervised Learning | Cybersecurity | RSK World

Network Anomaly Detection Project - RSK World

Machine Learning Anomaly Detection Python Unsupervised Learning Scikit-learn Network Security

This project implements a Network Traffic Anomaly Detection System using unsupervised machine learning algorithms. It employs Isolation Forest and Autoencoder models to identify unusual network traffic patterns, potential security breaches, and network intrusions. The system analyzes network flow characteristics, extracts meaningful features from network packets, and provides comprehensive anomaly detection with ensemble methods, threshold optimization, and real-time detection capabilities.

If you find this project useful, you can support with a small contribution.

Secure Fast Trusted

Pay via UPI QR

Scan or tap an amount to auto-generate

₹

Open UPI app

GPay PhonePe Paytm

Download Free Source Code

Network Flow Analysis

Comprehensive network traffic data preprocessing and analysis to identify unusual patterns and potential security breaches.

Network flow data preprocessing
Traffic pattern analysis
Flow characteristic extraction
Data normalization and cleaning

Isolation Forest Algorithm

Fast tree-based anomaly detection algorithm for identifying outliers in network traffic patterns.

Fast anomaly detection
Tree-based approach
Unsupervised learning
High performance on large datasets

Autoencoder Deep Learning

Neural network-based autoencoder for complex pattern detection and deep anomaly identification in network traffic.

Deep learning approach
Complex pattern detection
Neural network architecture
TensorFlow/Keras implementation

Ensemble Detection

Combines Isolation Forest and Autoencoder models for improved accuracy and robust anomaly detection.

Multiple voting strategies
Weighted ensemble approach
Improved accuracy
Model combination techniques

Jupyter Notebooks

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

Data analysis notebook
Model training notebook
Evaluation notebook
Step-by-step tutorials

Feature Engineering

Advanced feature extraction from network packets including flow characteristics, packet statistics, and traffic patterns.

Network packet analysis
Flow feature extraction
Statistical feature computation
Traffic pattern identification

Threshold Optimization

Automatically finds optimal detection thresholds using F1-score maximization and precision-recall balance.

F1-score maximization
Precision-recall balance
Custom metric optimization
Multiple threshold strategies

Real-time Detection

Process streaming network traffic in real-time with pre-trained models and batch processing capabilities.

Single sample prediction
Batch processing support
Sliding window analysis
Alert generation

Model Persistence

Save and load trained models for production deployment and reuse.

Model serialization
Pickle format support
Model versioning
Easy model deployment

Anomaly Explanation

Explains why specific samples are flagged as anomalies with feature importance analysis and detailed reports.

Feature importance analysis
Z-score based deviation
Detailed explanation reports
Permutation importance

Data Preprocessing

Robust data preprocessing pipeline for network traffic dataset preparation and feature engineering.

Dataset loading and cleaning
Network flow preprocessing
Train/test split
Feature scaling and normalization

Visualization Tools

Comprehensive visualization utilities for model performance, metrics, and analysis including ROC and PR curves.

ROC and Precision-Recall curves
Anomaly score distributions
2D PCA projections
Cluster visualizations

Anomaly Clustering

Groups similar anomalies to identify attack patterns using DBSCAN and K-Means clustering algorithms.

DBSCAN clustering
K-Means clustering
Cluster pattern analysis
Visual cluster representation

Utility Functions

Helper functions for network data parsing, feature extraction, model evaluation, and common development tasks.

Network data parsing utilities
Feature extraction helpers
Model evaluation functions
Data formatting utilities

Requirements

The following are the technical requirements for this project:

Python 3.8+
Scikit-learn 1.2.0+
TensorFlow/Keras 2.0+
Pandas 1.5.0+
NumPy 1.20.0+
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
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
Network Anomaly Detection Documentation

Additional Sponsored Content

Download Free Source Code

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

Download Free Source Code

Technologies

Python 3.8+

Scikit-learn

TensorFlow/Keras

Pandas

ML Projects

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

View Project

View All Projects

Theme Settings

Color Scheme

Display Options

Font Size

Network Traffic Anomaly Detection Machine Learning

Network Flow Analysis

Isolation Forest Algorithm

Autoencoder Deep Learning

Ensemble Detection

Jupyter Notebooks

Feature Engineering

Threshold Optimization

Real-time Detection

Model Persistence

Anomaly Explanation

Data Preprocessing

Visualization Tools

Anomaly Clustering

Utility Functions

Requirements

Credits & Acknowledgments

Support & Contact

Featured Content

Featured Content

Featured Content

Additional Sponsored Content

Download Free Source Code

Quick Links

Categories

Technologies

Explore More ML Projects

Fraud Detection System using ML

Network Traffic Anomaly Detection

Password Strength Prediction with ML

Ransomware Detection with Deep Learning

Botnet Detection with Machine Learning