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

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.

Creating an Intrusion Detection System

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

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

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.

Network Intrusion Detection System

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

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

Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

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