dongyou123

dongyou123's Stars

• 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.

  Language:Jupyter Notebook3819

• usualwitch/BiLSTM-CNN-Pytorch

  BiLSTM-CNN for Chinese text classification.

  Language:Python151

• omar-rh/NIDS-based-on-DL-with-imbalanced-NSL-KDD

  the DL methods used are : DNN, CNN, LSTM, GRU, CNN-LSTM, CNN-GRU, CNN-BiLSTM, CNN-BiGRU, the dataset used is : imbalanced NSL-KDD (KDDTrain+, KDDTest20+)

  Language:Jupyter Notebook333

• liguge/DLWCB

  基于Laplace小波卷积和BiGRU的少量样本故障诊断方法 (Small sample fault diagnosis based on Laplace wavelet convolution and BiGRU)

  Language:Python437

• munhouiani/LuNet

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

  Language:Python516

• ZZYINGing/Two_Level_IoT_Network_Attack_Detection_System_Using_Deep_Learning

  In this project, I developed a two level IoT network attack detection system with 1D-CNN. The level-1 model classifies the network flow as normal or attacks, while the level-2 model identifies the type of attacks. I evaluated the proposed model on the UNSW-NB15 dataset to show the effectiveness of my method. The proposed model is evaluated using Ac

  21

• Lovish-Dak/Network-Intrusion-Detection-System

  In this project, I created a network intrusion detection system using CNN and BiLSTM layers. I trained the model on NSL-KDD & UNSW-NB15 dataset. I used some pre-processing techniques such as Min-Max Normalization, One-Hot Encoding etc. and measured the performance of the model on metrics such as accuracy, FPR, FNR, Precision, Recall & F-1 Score

  Language:Jupyter Notebook6

• Arjun-V-K-20BAI1182/Network-Intrusion-Detection-System-using-Machine-Learning-LCCDE-on-UNSW-NB15

  Language:Jupyter Notebook1

• khairulislam/Anomaly-Detection-on-UNSW-NB15

  An ensemble based machine learning for anomaly detection from the network traffic.

  Language:TeX31

• FlamingJay/Time-related-Intrusion-Detection-Model-based-on-Recurrent-Neural-Network

  Here, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used.

  Language:Python6419

• abhinav-bhardwaj/IoT-Network-Intrusion-Detection-System-UNSW-NB15

  Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

  Language:Jupyter Notebook12835

• TGyAlDeen/IDS-UNSW-NB15

  IoT intrusion Detection Model based on neural network and random forests

  Language:Jupyter Notebook4319

• fatemehm/Federated-Learning-IDS

  Language:Jupyter Notebook72

• MosabHamdan12/nomaly-based-Intrusion-Detection-Technique-for-IoT-Enabled-Smart-Cities

  This study proposes a two- level classification technique for the anomaly detection-based IDS architecture for fog-edge sides. Targeted for IoT-smart city networks, the upper layer network uses a gradient boosting classifier while the lower layer network employs deep learning (DL) based on the combination of a long-short-term memory and a convolutional neural network (CNN-LSTM).

  Language:Jupyter Notebook133

• polvalls9/Transfer-Learning-Based-Intrusion-Detection-in-5G-and-IoT-Networks

  Development of a transfer learning system for the detection of cyber-attacks in 5G and IoT networks. Transfer learning will improve the accuracy and precision of cyber-attacks detection in 5G /IoT networks with limited computing capability and datasets with scarce labeled data.

  Language:Jupyter Notebook126

• alpersarikaya/grugbm

  Language:Jupyter Notebook11

• ayeshasdina/Intrusion-Detection-IoT

  Language:Python5

• dekinks/DeepLearningSecurity

  Language:Jupyter Notebook21

• rohit11544/gru-nsl-kdd

  Language:Jupyter Notebook2

• igorruys/nsl-kdd-attack-detection

  Language:Jupyter Notebook1

• ma1VAR3/Intrusion-Detection-using-Representation-Block-with-Specialized-Autoencoders

  Intrusion Detection on NSL-KDD dataset

  Language:Jupyter Notebook1

• caoloi/nsl_kdd_few_shot

  Few-Shot Learning for NSL-KDD Dataset

  1

• BhushanPawar-01/Itrusion_Detection_System_using_CNN

  This is a CNN model for prediction of cyber attack, trained using NSL-KDD dataset

  Language:Jupyter Notebook93

• WhiteHatCyberus/Deep-Learning-Evaluation-of-IDS-Datasets

  Deep Model Intrusion Detection (IDS) Evaluation of NSL KDD and CIC IDS 2018 datasets.

  Language:Jupyter Notebook10

• dSupertramp/nsl-kdd-project

  Progetto relativo alla materia "AI / Deep Learning".

  Language:Jupyter Notebook7

• PythonJulia/NSL-KDD-Data-Analysis

  NSL-KDD (for network-based intrusion detection systems (IDS)) is a dataset suggested to solve some of the inherent problems of the parent KDD'99 dataset. This IDS basically helps to determine security of systems and alarming when intrusion is noticed or detected. Choosing NSL-KDD provides insightful analysis using various machine learning algorithms for intrusion detection. Myself expecting to explore intuitive insights of intrusion detection and work on various machine learning algorithms that is reasonable to understand future instance of attacks and its types.

  Language:Jupyter Notebook112

• xiaochus/TrafficFlowPrediction

  Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

  Language:Python531237

• Kapildev-Pandey/Research_Paper-LSTM-Stacked-AE-to-Unsupervised-Multivariate-Forecasting

  Language:Jupyter Notebook41

• CLaraRR/autoencoder_practice

  AutoEncoder implements by keras. Including AE, DAE, DAE_CNN, VAE, VAE_CNN, CVAE, Sparse AE, Stacked DAE.

  Language:Python3910

• qinguoyi/TinyWebServer

  :fire: Linux下C++轻量级WebServer服务器

  Language:C++16.3k3.9k