Z-wenfeng's Stars
r7sy/IntrusionDetection
This repository contains a notebook implementing an autoencoder based approach for intrusion detection, the full documentation of the study will be available shortly.
podgorskiy/ALAE
[CVPR2020] Adversarial Latent Autoencoders
jeecgboot/JeecgBoot
🔥「企业级低代码平台」前后端分离架构SpringBoot 2.x/3.x，SpringCloud，Ant Design&Vue3，Mybatis，Shiro，JWT。强大的代码生成器让前后端代码一键生成，无需写任何代码! 引领新的开发模式OnlineCoding->代码生成->手工MERGE，帮助Java项目解决70%重复工作，让开发更关注业务，既能快速提高效率，帮助公司节省成本，同时又不失灵活性。
dhelmr/bachelor-thesis
Comparing Anomaly-Based Network Intrusion Detection Approaches Under Practical Aspects
Rocionightwater/ML-NIDS-for-SCADA
In this work, we aim at developing a NIDS (Network Intrusion Detection System) that detects attacks targeting SCADA systems, in a concrete industrial used case scenario.
CharlesMure/cassiope-NIDS
Creating a NIDS based on a Deep Neural Network (CNN)
keras-team/keras
Deep Learning for humans
TGyAlDeen/IDS-UNSW-NB15
IoT intrusion Detection Model based on neural network and random forests
InitRoot/UNSW_NB15
Feature coded UNSW_NB15 intrusion detection data.
vinayakumarr/Network-Intrusion-Detection
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
Moskari/ITKST42-network-data-classifier
A network data classifier for UNSW-NB15 data set. This is an university course work for "ITKST42 Information Security Technology".
tjnel/DSU_INSuRE_SP19_IDS_Prioritization
IDS Alert Prioritization INSuRE Research Project
rishabh-mondal/Evaluating-Shallow-and-Deep-Neural-Networks-for-Network-Intrusion-Detection-Systems-
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-’99’ dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
AnushaUpadhyaya/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.
locnguyen21/Deep-Learning-for-IDS
Using NSL_KDD data
shramos/Awesome-Cybersecurity-Datasets
A curated list of amazingly awesome Cybersecurity datasets
mehrdadep/deep-learning-nids
A Deep Learning approach toward creating a NIDS using python
tamimmirza/Intrusion-Detection-System-using-Deep-Learning
VGG-19 deep learning model trained using ISCX 2012 IDS Dataset
Parker-Lyu/TensorFLow-Learning
B站上炼数成金的公开课笔记