perseuslee Goto Github PK

attention_network_with_keras

An example attention network with simple dataset.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

cnninpytorch

一个PyTorch搭建CNN的中文基础教程

deep-learning-and-rare-event-prediction

Deep Learning and Rare Event Prediction

detect-lstm-model

检测恶意 URL and Request (Bi-LSTM、Bi-LSTM + CNN、CNN + Bi-LSTM、CNN + Bi-LSTM + CNN)

diffpriv

Easy differential privacy in R

dysat_pytorch

Pytorch implementation of DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks

gnnguard

Defending graph neural networks against adversarial attacks (NeurIPS 2020)

industry-analysis

Multi-variate time series forecasting using ML algorithms

infocom2020-guardian

Official code for the INFOCOM 2020 paper "Guardian: Evaluating Trust in Online Social Networks with Graph Convolutional Networks."

kats-tutorials

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

lstm-recurrent-neural-network

A deep learning project: RNN and its implementation in anomoly detection

malicious-traffic-detection-classification

The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

ml-and-dp

mynote

用于存放学习笔记

neural-networks-for-time-series-analysis

Compare how ANNs, RNNs, LSTMs, and LSTMs with attention perform on time-series analysis

privacy

Library for training machine learning models with privacy for training data

privatemultiplicativeweights.jl

Differentially private synthetic data

rappor

RAPPOR: Privacy-Preserving Reporting Algorithms

sprint_gan

Privacy-preserving generative deep neural networks support clinical data sharing

stacked_bidirectional_unidirectional_lstm

Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network

study-notes-on-time-series

My study notes on time series. Will keep updating.

time-series-analysis--arima

A study journal of time series analysis: ARIMA algorithm. With market sales dataset practice.

traffic_graph_convolutional_lstm

Traffic Graph Convolutional Recurrent Neural Network

trustguard

Source code of paper "TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support"

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