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

Overview

logdeep

Introduction

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

Framework of logdeep

Note: This repo does not include log parsing,if you need to use it, please check logparser

Major features

  • Modular Design

  • Support multi log event features out of box

  • State of the art(Including resluts from deeplog,loganomaly,robustlog...)

Models

Model Paper reference
DeepLog [CCS'17] DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
LogAnomaly [IJCAI'19] LogAnomaly: UnsupervisedDetectionof SequentialandQuantitativeAnomaliesinUnstructuredLogs
RobustLog [FSE'19] RobustLog-BasedAnomalyDetectiononUnstableLogData

Requirement

  • python>=3.6
  • pytorch >= 1.1.0

Quick start

git clone https://github.com/donglee-afar/logdeep.git
cd logdeep

Example of building your own log dataset
SAMPLING_EXAMPLE.md

Train & Test DeepLog example

cd demo
# Train
python deeplog.py train
# Test
python deeplog.py test

The output results, key parameters and train logs will be saved under result/ path

DIY your own pipeline

Here is an example of the key parameters of the loganomaly model which in demo/loganomaly.py
Try to modify these parameters to build a new model!

# Smaple
options['sample'] = "sliding_window"
options['window_size'] = 10

# Features
options['sequentials'] = True
options['quantitatives'] = True
options['semantics'] = False

Model = loganomaly(input_size=options['input_size'],
                    hidden_size=options['hidden_size'],
                    num_layers=options['num_layers'],
                    num_keys=options['num_classes'])

Benchmark results

HDFS
Model feature Precision Recall F1
DeepLog(unsupervised) seq 0.9583 0.9330 0.9454
LogAnomaly(unsupervised) seq+quan 0.9690 0.9825 0.9757
RobustLog(supervised) semantic 0.9216 0.9586 0.9397
Owner
donglee
Kaggle Master
donglee
[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

SMYRF: Efficient attention using asymmetric clustering Get started: Abstract We propose a novel type of balanced clustering algorithm to approximate a

Giannis Daras 46 Dec 22, 2022
Fast, flexible and fun neural networks.

Brainstorm Discontinuation Notice Brainstorm is no longer being maintained, so we recommend using one of the many other,available frameworks, such as

IDSIA 1.3k Nov 21, 2022
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper

Jianwei Yang 278 Dec 25, 2022
AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models

AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models Description

Angel de Paula 0 Jun 08, 2022
Scikit-learn compatible estimation of general graphical models

skggm : Gaussian graphical models using the scikit-learn API In the last decade, learning networks that encode conditional independence relationships

213 Jan 02, 2023
MINERVA: An out-of-the-box GUI tool for offline deep reinforcement learning

MINERVA is an out-of-the-box GUI tool for offline deep reinforcement learning, designed for everyone including non-programmers to do reinforcement learning as a tool.

Takuma Seno 80 Nov 06, 2022
CATE: Computation-aware Neural Architecture Encoding with Transformers

CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans

16 Dec 27, 2022
LWCC: A LightWeight Crowd Counting library for Python that includes several pretrained state-of-the-art models.

LWCC: A LightWeight Crowd Counting library for Python LWCC is a lightweight crowd counting framework for Python. It wraps four state-of-the-art models

Matija Teršek 39 Dec 28, 2022
Simple (but Strong) Baselines for POMDPs

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs Welcome to the POMDP world! This repo provides some simple baselines for POMDPs, specific

Tianwei V. Ni 172 Dec 29, 2022
Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet

Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet, CVPR2021 安全AI挑战者计划第六期:ImageNet无限制对抗攻击 决赛第四名(team name: Advers)

51 Dec 01, 2022
This repository contains all source code, pre-trained models related to the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator"

An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator This is a Pytorch implementation for the paper "An Empirical Study o

Cuong Nguyen 3 Nov 15, 2021
Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.

Object DGCNN & DETR3D This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110

Wang, Yue 539 Jan 07, 2023
AdelaiDepth is an open source toolbox for monocular depth prediction.

AdelaiDepth is an open source toolbox for monocular depth prediction.

Adelaide Intelligent Machines (AIM) Group 743 Jan 01, 2023
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification

next_best_view_rl Setup Clone the repository: git clone --recurse-submodules ... In 'third_party/zed-ros-wrapper': git checkout devel Install mujoco `

Christian Korbach 1 Feb 15, 2022
Predicting 10 different clothing types using Xception pre-trained model.

Predicting-Clothing-Types Predicting 10 different clothing types using Xception pre-trained model from Keras library. It is reimplemented version from

AbdAssalam Ahmad 3 Dec 29, 2021
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you

Yu Meng 38 Dec 12, 2022
NeuralForecast is a Python library for time series forecasting with deep learning models

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate m

Nixtla 1.1k Jan 03, 2023
DeepAL: Deep Active Learning in Python

DeepAL: Deep Active Learning in Python Python implementations of the following active learning algorithms: Random Sampling Least Confidence [1] Margin

Kuan-Hao Huang 583 Jan 03, 2023
Deep-Learning-Image-Captioning - Implementing convolutional and recurrent neural networks in Keras to generate sentence descriptions of images

Deep Learning - Image Captioning with Convolutional and Recurrent Neural Nets ========================================================================

23 Apr 06, 2022
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"

Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati

Yang Li 12 May 30, 2022