1170300423

Web-traffic-anomaly-detection-using-C-LSTM-neural-networks

This project aims to detect the anomalies in Web-Traffic using a C-LSTM architecture.

AnomalyDetectionTimeSeriesData

Anamoly Detection in Time Series data of S&P 500 Stock Price index (of top 500 US companies) using Keras and Tensorflow

Getting-Things-Done-with-Pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Deep-Learning-For-Hackers

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

LSTM-Autoencoder-for-Anomaly-Detection

AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow

SWAT_data_Attack_Prediction

Using SWAT(Secure water treatment testbed) data to predict when the system is under attack. We are using 6 known types of attacks to apply machine learning algorithm

USAD-on-WADI-and-SWaT

Unofficial implementation of the KDD2020 paper "USAD: UnSupervised Anomaly Detection on multivariate time series" on two datasets cited in the papers, "SWaT" (Secure Water Treatment) and "WADI" (Water Distribution)

Anomaly-Detection-with-Swat-Dataset

Develope novel security metric using Deep-Learning to detect anomaly attacks into the critical infrastructure systems. This metric will be tested by Secure Water Treatment (SWaT) Dataset.

Link-Prediction

链路预测学习代码整理

pytorch-sentiment-classification

LSTM and CNN sentiment analysis

GraphAnomalyDetectionDatasets

用于图异常检测的数据集

anomaly_detection

基于LSTM的异常检测

IoT-Intrusion-Detection-System

Two staged IDS specific to IoT networks where Signature based IDS and Anomaly based IDS which is trained and classified using machine learning in this case CNN-LSTM is used together in component based architecture.

Anomaly-Detection

Anomaly detection using CNN-LSTM

traffic-prediction

traffic-prediction using LSTM and GCN by pytorch

Traffic-Forecasting-using-Graph-Convolution-LSTM-model

Traffic Forecasting using Graph Convolution + LSTM model is a ML model developed during the learning process of GCN. The primary soorce of this project is https://github.com/stellargraph/stellargraph

correlation_gcn_lstm_prediction

A stock prediction based on correlation adjencency matrix grapha and combinantion of gnn and lstm

GCN-LSTM