There are 9 repositories under kdd99 topic.
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities
Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN
Solutions to kdd99 dataset with Decision tree and Neural network by scikit-learn
修改谷歌提供的样例量子卷积神经网络模型,基于KDD99数据集进行训练,实现了网络攻击分类检测。
This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.
Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.
using machine-learning to detecte instruction
Cyber-attack classification in the network traffic database using NSL-KDD dataset
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
Kdd99 dataset analyzing and some data reproducing experiments with SDN
An Intrusion Detection System (IDS) implemented in Python, which utilizes machine learning techniques and the KDD Cup 1999 dataset to detect and classify network intrusions in real-time.
COSC 490 Towson University
Assess various ML algorithms on KDD99 network dataset then apply the best algorithm (Random Forest) using R.
This is the strong baseline in final competition (KDD1999) of NTU Machine Learning 2016 Fall lectured by Hung-Yi Lee
INTRUSION DETECTOR | ML
An introductory course to pandas and scikit learn
Creating an Intrusion Detection System
Intrusion detection using machine learning for KDD 99 dataset
A Tensorflow model to detect network intrusions in the KDD Cup 1999 data-set.
Network Anomaly Detection Using Deep Neural Network
Artificial Intelligence and Cybersecurity
Project for Kernel-Based Machine Learning and Multivariate Modelling course at UPC Barcelona (FIB)
Unsupervised (Clustering) Anomaly detection on Network Traffic Activity using a dataset from the annual KDD Cup competition
Intrusion Detection (KDD Cup 1999 Dataset) using Perceptron and Random Forest. UniFi AI final exam.
Anomaly detection by kmeans clustering applied on network traffic log files to train a model to detect fraudulant network behaviour
Anomaly detection using machine learning
A predictive model capable of distinguishing between bad connections and good connections using machine learning.
Network intrusion detection using the KDD Cup 1999 dataset. Explores multiple ML models and combines KNN with Random Forest for optimal results.
Project developed during Network Security class at Federal University of Rio de Janeiro on spring 2017