# Feature Selection for Fault Detection and Prediction based on Event Log Analysis

arXiv: https://arxiv.org/abs/2208.09440 · HTML: https://mlsanity.com/paper/2208.09440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://mlsanity.com/paper/2202.04301/) (2022)
- [Tools and Benchmarks for Automated Log Parsing](https://mlsanity.com/paper/1811.03509/) (2018)

## Built on by (most cited first, 3 of 3)

- [Graph Neural Networks based Log Anomaly Detection and Explanation](https://mlsanity.com/paper/2307.00527/) (2023)
- [ROSFD: Robust Online Streaming Fraud Detection with Resilience to Concept Drift in Data Streams](https://mlsanity.com/paper/2504.10229/) (2025)
- [Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis](https://mlsanity.com/paper/2605.06330/) (2026)

## Abstract (arXiv)

Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.