flya2 Goto Github PK

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This is the repo of the research paper, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security".

--2-intrusion-detection-system-using-machine-learning

Code for intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

-docker-python

Kaggle Python docker image

-intrusion-detection-system-using-cnn-and-transfer-learning

Code for intrusion detection system (IDS) development using CNN models and transfer learning

1-

Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies

11-cnn-cicids

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.

2019-fence_gan

Fence GAN: Towards Better Anomaly Detection

222-efficient-cnn-bilstm-for-network-ids

222-Efficient-CNN-BiLSTM-for-Network-IDS

anomalydae-11

AnomalyDAE (ICASSP2020)

awesome-deeplearning

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

bert

TensorFlow code and pre-trained models for BERT

char-models-15

Character level models for sentiment analysis

cicid-

Machine Learning in Cybersecurity

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

e-resgat--

The pytorch implementation of E-GraphSAGE and E-ResGAT, two solutions for intrusion detection.

edge-detect

Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network".

flya2.github.io

front-end-notes

前端学习笔记

gnn-nids

handwriting-synthesis

Handwriting Synthesis with RNNs ✏️

keras

Deep Learning for humans

lstm-human-activity-recognition-

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

lunet-cnn-rnn-

Pytorch implementation of LuNet: A Deep Neural Network for Network Intrusion Detection

mstream-c-

Anomaly Detection on Time-Evolving Streams in Real-time. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

network-intrusion-detection-

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

project_01

这是一个test 仓库

project_02

这是一个测试的仓库

pygod--

A Python Library for Graph Outlier Detection (Anomaly Detection)

pytorch-kaldi-

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.