Loglizer

Abstract

1. Introduction

Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information during system operation that allows developers and support engineers to monitor their systems and track abnormal behaviors and errors. Loglizer provides a toolkit that implements a number of machine-learning based log analysis techniques for automated anomaly detection.

2. Objective

A machine learning toolkit for log-based anomaly detection [ISSRE'16]

This project demonstrates how Jupyter Notebook, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Log collection: Logs are generated at runtime and aggregated into a centralized place with a data streaming pipeline, such as Flume and Kafka.
• Anomaly detection: Anomaly detection models are trained to check whether a given feature vector is an anomaly or not.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/logpai/loglizer.git
cd loglizer

git clone https://github.com/logpai/loglizer.git
cd loglizer
pip install -r requirements.txt

# Load HDFS dataset. If you would like to try your own log, you need to rewrite the load function.
(x_train, y_train), (x_test, y_test) = dataloader.load_HDFS(...)

# Feature extraction and transformation
feature_extractor = preprocessing.FeatureExtractor()
feature_extractor.fit_transform(...)

# Model training
model = PCA()
model.fit(...)

# Feature transform after fitting
x_test = feature_extractor.transform(...)
# Model evaluation with labeled data
model.evaluate(...)

# Anomaly prediction
x_test = feature_extractor.transform(...)
model.predict(...) # predict anomalies on given data

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by logpai and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.