A modular application for performing anomaly detection in networks

Last update: Dec 9, 2021

Overview

Deep-Learning-Models-for-Network-Annomaly-Detection

The modular app consists for mainly three annomaly detection algorithms. The system supports models both in Tensorflow and Pytorch.

Annomaly DAE
DeepSphere
VAE

The key aspect is extensibilty and the ability to add more models as required. The repository structure is as follows.

Paper list of log-based anomaly detection

411 Dec 5, 2022

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

32 Oct 26, 2022

Demo project for real time anomaly detection using kafka and python

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36 Dec 12, 2022

Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

268 Dec 22, 2022

MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

61 Dec 2, 2022

USAD - UnSupervised Anomaly Detection on multivariate time series

USAD - UnSupervised Anomaly Detection on multivariate time series Scripts and utility programs for implementing the USAD architecture. Implementation

116 Jan 4, 2023

LogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.

279 Dec 13, 2022

Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

102 Dec 26, 2022

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows WACV 2022 preprint:https://arxiv.org/abs/2107.1

156 Dec 28, 2022

Owner

Shivam Patel

Visiting Student at Cambridge University. Active in Applied AI , Mathematical Modeling and Optimization and also a full stack developer.

GitHub

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

92 Dec 3, 2022

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181 Dec 18, 2022

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24 Nov 14, 2022