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anomaly-detection-kdd99-cnnlstm

Intrusion Detection System using Machine Learning and Deep Learning

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

clustering-based-anomaly-detection

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

deeplog

Pytorch implementation of DeepLog.

deeplog-1

This is the realization of core DeepLog

dirsearch

Web path scanner

gan_tensorflow_eager

gans

Generative Adversarial Networks implemented in PyTorch and Tensorflow

hdbscan

A high performance implementation of HDBSCAN clustering.

ids-using-cnn-rnn

Intrusion detection system using CNN and RNN

ijson

Iterative JSON parser with Pythonic interface

introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

intrusion-detection

Unsupervised Learning Intrusion Detection System

intrusion-detection-system

I have tried some of the machine learning and deep learning algorithm for IDS 2017 dataset. The link for the dataset is here: http://www.unb.ca/cic/datasets/ids-2017.html. By keeping Monday as the training set and rest of the csv files as testing set, I tried one class SVM and deep CNN model to check how it works. Here the Monday dataset contains only normal data and rest of the days contains both normal and attacked data. Also, from the same university (UNB) for the Tor and Non Tor dataset, I tried K-means clustering and Stacked LSTM models in order to check the classification of multiple labels.

intrusiondetection

This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.

logdeep

log anomaly detection toolkit including DeepLog

loglizer

A log analysis toolkit for automated anomaly detection [ISSRE'16]

logparser

A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]

lstm

Minimal, clean example of lstm neural network training in python, for learning purposes.

mad-gans

Applied generative adversarial networks (GANs) to do anomaly detection for time series data

mall

mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于SpringBoot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。

meta-critic-networks

Pytorch code for Arxiv Paper: Learning to learn: Meta-Critic Networks for Sample-Efficient Learning

network-intrusion-detection

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

network-intrusion-detection-using-machine-learning-techniques

Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.

nids_sndae

Network Intrusion Detection System Using Stacked Non Symmetric Autoencoder and Random Forest

nsl_kdd

NSL-KDD Dataset

omnianomaly

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

oneforall

OneForAll是一款功能强大的子域收集工具

plelog

Implementation of PLELog in ICSE 2021 accepted paper:Semi-supervised Log-based Anomaly Detection via Probabilistic Label Estimation.