PLELog

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

• PLELog
  • Description
  • Project Structure
  • Datasets
  • Reproducibility:
    • Environment:
    • Preparation
  • Anomaly detection
  • 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
BGL	Blue Gene/L supercomputer log	214.7 days	4,747,963	708.76MB	Usenix-CFDR Data

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, please refer to the requirements.txt file for package installation.

• According to issue#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 , 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

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, 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.

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