Log-based Anomaly Detection for Unseen Logs via Source Code Feature Extraction
Drill.ipynb: this file is the evaluation part of the paper IPDPS'23 Log-based Anomaly Detection for Unseen Logs via Source Code Feature Extraction
features.pkl: this file is the sentiment and context features of HDFS log statements.
data: this directory contains the log sessions of HDFS, the corresponding log templates of log indices are described in Drill.ipynb.
figures.ipynb: this file displays the figures in the paper.
sentilog: this directory contains the code of paper HotStorage'21 SentiLog: Anomaly Detecting on Parallel File Systems via Log-based Sentiment Analysis., which shows how we extract the sentiment features in Drill.
pytorch: pip3 install torch
d2l: pip3 install d2l
sklearn: pip3 install scikit-learn
jupyter notebook: pip3 install notebook
jupyter notebook Drill.ipynb