Initial commit

__pycache__

__init__.py

.idea

*.log

datasets/*

outputs/

!datasets

 No newline at end of file

import sys

sys.path.extend([".", ".."])

import os, gc, math, abc, pickle, argparse

import random

import time

import hashlib

import numpy as np

import torch

import datetime

import torch.nn as nn

import torch.nn.functional as F

from torch.utils.data import TensorDataset, DataLoader

from collections import Counter

from tqdm import *

import regex as re

import logging

from collections import Counter

from torch.nn.parameter import Parameter

from multiprocessing import Manager, Pool

seed = 6

random.seed(seed)  # Python random module.

np.random.seed(seed)  # Numpy module.

torch.manual_seed(seed)  # 为CPU设置随机种子

torch.cuda.manual_seed(seed)  # 为当前GPU设置随机种子

torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU，为所有GPU设置随机种子

torch.backends.cudnn.benchmark = False

torch.backends.cudnn.deterministic = True

# Device configuration

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

SESSION = hashlib.md5(

    time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(time.time() + 8 * 60 * 60)).encode('utf-8')).hexdigest()

SESSION = 'SESSION_' + SESSION

def GET_PROJECT_ROOT():

    # goto the root folder of LogBar

    current_abspath = os.path.abspath('__file__')

    while True:

        if os.path.split(current_abspath)[1] == 'PLELog':

            project_root = current_abspath

            break

        else:

            current_abspath = os.path.dirname(current_abspath)

    return project_root

def GET_LOGS_ROOT():

    log_file_root = os.path.join(GET_PROJECT_ROOT(), 'logs')

    if not os.path.exists(log_file_root):

        os.makedirs(log_file_root)

    return log_file_root

LOG_ROOT = GET_LOGS_ROOT()

PROJECT_ROOT = GET_PROJECT_ROOT()

pretrained_mode_path = os.path.join(PROJECT_ROOT, 'outputs/models/pretrain')

if not os.path.exists(pretrained_mode_path):

    os.makedirs(pretrained_mode_path)

os.environ['TRANSFORMERS_CACHE'] = pretrained_mode_path

os.environ['TRANSFORMERS_OFFLINE'] = '1'

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# PLELog 

 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5910349.svg)](https://doi.org/10.5281/zenodo.5910349)

This is the basic implementation of our submission in ICSE 2021: **Semi-supervised Log-based Anomaly Detection via Probabilistic Label Estimation**.

- [PLELog](#plelog)

  * [Description](#description)

  * [Project Structure](#project-structure)

  * [Datasets](#datasets)

  * [Reproducibility:](#reproducibility-)

    + [Environment:](#environment-)

    + [Preparation](#preparation)

  * [Anomaly detection](#anomaly-detection)

  * [Contact](#contact)

## Description

`PLELog` is a novel approach for log-based anomaly detection via probabilistic label estimation. 

It is designed to effectively detect anomalies in unlabeled logs and meanwhile avoid the manual labeling effort for training data generation.

We use semantic information within log events as fixed-length vectors and apply `HDBSCAN` to automatically clustering log sequences. 

After that, we also propose a Probabilistic Label Estimation approach to reduce the noises introduced by error labeling and put "labeled" instances into `attention-based GRU network` for training. 

We conducted an empirical study to evaluate the effectiveness of `PLELog` on two open-source log data (i.e., HDFS and BGL). The results demonstrate the effectiveness of `PLELog`. 

In particular, `PLELog` has been applied to two real-world systems from a university and a large corporation, further demonstrating its practicability.

## Project Structure

```

├─approaches  # PLELog main entrance.

├─config      # Configuration for Drain

├─entities    # Instances for log data and DL model.

├─utils

├─logs        

├─datasets    

├─models      # Attention-based GRU and HDBSCAN Clustering.

├─module      # Anomaly detection modules, including classifier, Attention, etc.

├─outputs           

├─parsers     # Drain parser.

├─preprocessing # preprocessing code, data loaders and cutters.

├─representations # Log template and sequence representation.

└─util        # Vocab for DL model and some other common utils.

```

## Datasets

We used `2` open-source log datasets, HDFS and BGL. 

In the future, we are planning on testing `PLELog` on more log data.

| Software System | Description                        | Time Span  | # Messages | Data Size | Link                                                      |

|       ---       |           ----                     |    ----    |    ----    |  ----     |                ---                                        |

| HDFS            | Hadoop distributed file system log | 38.7 hours | 11,175,629 | 1.47 GB   | [LogHub](https://github.com/logpai/loghub)                |

| BGL             | Blue Gene/L supercomputer log      | 214.7 days | 4,747,963  | 708.76MB  | [Usenix-CFDR Data](https://www.usenix.org/cfdr-data#hpc4) |

## Reproducibility

We have published an full version of PLELog (including HDFS log dataset, glove word embdding as well as a trained model) in Zenodo, please find the project from the zenodo badge at the beginning.

### Environment

**Note:** 

- We attach great importance to the reproducibility of `PLELog`. Here we list some of the key packages to reproduce our results. However, as discussed in [issue#14](https://github.com/YangLin-George/PLELog/issues/14), please refer to the `requirements.txt` file for package installation.

- According to [issue#16](https://github.com/YangLin-George/PLELog/issues/16), there seems to have some problem with suggested hdbscan version, if your environment has such an error, please refer to the issue for support. Great thanks for this valuable issue!

- According to [issue#19 ](https://github.com/YangLin-George/PLELog/issues/19), remove numpy version requirements from `requirements.txt` file. Great thanks for this suggestion!

**Key Packages:**

PyTorch v1.10.1

python v3.8.3

hdbscan v0.8.27

overrides v6.1.0

**scikit-learn v0.24**

tqdm

regex

[Drain3](https://github.com/IBM/Drain3)

hdbscan and overrides are not available while using anaconda, try using pip or:

`conda install -c conda-forge pkg==ver` where `pkg` is the target package and `ver` is the suggested version.

**Please be noted:** Since there are some known issue about joblib, scikit-learn > 0.24 is not supported here. We'll keep watching. 

### Preparation

You need to follow these steps to **completely** run `PLELog`.

- **Step 1:** To run `PLELog` on different log data, create a directory under `datasets` folder **using unique and memorable name**(e.g. HDFS and BGL). `PLELog` will try to find the related files and create logs and results according to this name.

- **Step 2:** Move target log file (plain text, each raw contains one log message) into the folder of step 1.

- **Step 3:** Download `glove.6B.300d.txt` from [Stanford NLP word embeddings](https://nlp.stanford.edu/projects/glove/), and put it under `datasets` folder.

- **Step 4:** Run `approaches/PLELog.py` (make sure it has proper parameters). You can find the details about Drain parser from [IBM](https://github.com/IBM/Drain3).

**Note:** Since log can be very different, here in this repository, we only provide the processing approach of HDFS and BGL w.r.t our experimental setting.

## Anomaly Detection

To those who are interested in applying PLELog on their log data, please refer to `BasicLoader` abstract class in preprocessing/BasicLoader.py` for more instructions.

- **Step 1:** To run `PLELog` on different log data, create a directory under `datasets` folder **using unique and memorable name**(e.g. HDFS and BGL). `PLELog` will try to find the related files and create logs and results according to this name.

- **Step 2:** Move target log file (plain text, each raw contains one log message) into the folder of step 1.

- **Step 3:** Create a new dataloader class implementing `BasicLoader`. 

- **Step 4:** Go to `preprocessing/Preprocess.py` and add your new log data into acceptable variables.

## Contact

We are happy to see `PLELog` being applied in the real world and willing to contribute to the community. Feel free to contact us if you have any question!

Authors information:

| Name          | Email Address          | 

| ------------- | ---------------------- | 

| Lin Yang      | linyang@tju.edu.cn     |

| Junjie Chen * | junjiechen@tju.edu.cn  |

| Weijing Wang  | wangweijing@tju.edu.cn |

\* *corresponding author*