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.
usualwitch/BiLSTM-CNN-Pytorch
BiLSTM-CNN for Chinese text classification.
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+)
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)
munhouiani/LuNet
Pytorch implementation of LuNet: A Deep Neural Network for Network Intrusion Detection
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
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
Arjun-V-K-20BAI1182/Network-Intrusion-Detection-System-using-Machine-Learning-LCCDE-on-UNSW-NB15
khairulislam/Anomaly-Detection-on-UNSW-NB15
An ensemble based machine learning for anomaly detection from the network traffic.
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.
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
TGyAlDeen/IDS-UNSW-NB15
IoT intrusion Detection Model based on neural network and random forests
fatemehm/Federated-Learning-IDS
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).
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.
alpersarikaya/grugbm
ayeshasdina/Intrusion-Detection-IoT
dekinks/DeepLearningSecurity
rohit11544/gru-nsl-kdd
igorruys/nsl-kdd-attack-detection
ma1VAR3/Intrusion-Detection-using-Representation-Block-with-Specialized-Autoencoders
Intrusion Detection on NSL-KDD dataset
caoloi/nsl_kdd_few_shot
Few-Shot Learning for NSL-KDD Dataset
BhushanPawar-01/Itrusion_Detection_System_using_CNN
This is a CNN model for prediction of cyber attack, trained using NSL-KDD dataset
WhiteHatCyberus/Deep-Learning-Evaluation-of-IDS-Datasets
Deep Model Intrusion Detection (IDS) Evaluation of NSL KDD and CIC IDS 2018 datasets.
dSupertramp/nsl-kdd-project
Progetto relativo alla materia "AI / Deep Learning".
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.
xiaochus/TrafficFlowPrediction
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
Kapildev-Pandey/Research_Paper-LSTM-Stacked-AE-to-Unsupervised-Multivariate-Forecasting
CLaraRR/autoencoder_practice
AutoEncoder implements by keras. Including AE, DAE, DAE_CNN, VAE, VAE_CNN, CVAE, Sparse AE, Stacked DAE.
qinguoyi/TinyWebServer
:fire: Linux下C++轻量级WebServer服务器