Machine Learning Algorithms on NSL-KDD dataset

NSLKDD Dataset for WEKA

This is a software application to detect network intrusion by monitoring a network or system for malicious activity and predicts whether it is Normal or Abnormal(attacked with intrusion classes like DOS/PROBE/R2L/U2R).

A comparison between Statistical, Machine Learning, PCA, SVD, and REF methods

Feature based analysis using ML classifiers on the NSL-KDD Dataset

AN Intrusion Detection System using LSTM deep learning model to detect anomalous network Integrated with SDN POX controller to analyze and threats in real time

Code for intrusion detection system based on "Intrusion Detection System Using Machine Learning Algorithms" tutorial on Geeksforgeeks and Intrusion Detection on NSL KDD Github repository.

This is a software application to detect network intrusion by monitoring a network or system for malicious activity and predicts whether it is Normal or Abnormal(attacked with intrusion classes like DOS/PROBE/R2L/U2R).

This is a software application to detect network intrusion by monitoring a network or system for malicious activity and predicts whether it is Normal or Abnormal(attacked with intrusion classes like DOS/PROBE/R2L/U2R).

Python-based tool designed to process network traffic packets and extract features compliant with the NSL-KDD dataset format.

Creación de un Sistema de detección de intrusiones utilizando BPSO y SVM

Comparative Analysis of Deep Learning and Machine Learning Models for Network Intrusion Detection

Creating an Intrusion Detection System

Network Intrusion Detection System using Machine Learning and Deep Learning

This project was an attempt to use ML techniques to identify and prevent DDOS attacks.

A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection

An automated tool for real-time feature engineering on network traffic data, optimized for intrusion detection using the NSL-KDD dataset. This tool processes live network traffic, extracts relevant features, and prepares data for use in machine learning models.

Interactive notebook implementing three unsupervised ML algorithms (Isolation Forest, LOF, Deep Autoencoder) on NSL-KDD dataset. Includes data preprocessing, EDA with PCA/t-SNE visualizations, model training, and comparative evaluation. Cloud-compatible for Google Colab and Kaggle with detailed performance metrics and anomaly score analysis.

Anomaly-Based Intrusion Detection System using Machine Learning (SVM & Neural Networks) on NSL-KDD and UNB-IDS 2018 datasets with adversarial robustness evaluation.

Network Intrusion Detection System