Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
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Updated
May 8, 2019 - Python
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
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 is a classification model with five classes (normal, DOS, R2L, U2R,PROBING). Ignore the content features of TCP connection ( columns 10-22 of KDD Cup 99 dataset) when training the model to adapt the project that a kdd99 feature extractor
using machine-learning to detecte instruction
Cyber-attack classification in the network traffic database using NSL-KDD dataset
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.
This is the strong baseline in final competition (KDD1999) of NTU Machine Learning 2016 Fall lectured by Hung-Yi Lee
Demo of SciKit ML algorithms using the kdd99 dataset
Network Anomaly Detection Using Deep Neural Network
Creating an Intrusion Detection System
Intrusion Detection (KDD Cup 1999 Dataset) using Perceptron and Random Forest. UniFi AI final exam.
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