# LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs

arXiv: https://arxiv.org/abs/2402.10397 · HTML: https://mlsanity.com/paper/2402.10397/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [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)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs](https://mlsanity.com/paper/2008.09340/) (2020)
- [A Taxonomy of Anomalies in Log Data](https://mlsanity.com/paper/2111.13462/) (2021)

## Built on by (most cited first, 4 of 4)

- [Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection](https://mlsanity.com/paper/2503.08045/) (2025)
- [ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model](https://mlsanity.com/paper/2505.13496/) (2025)
- [A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers](https://mlsanity.com/paper/2512.23380/) (2025)
- [ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling](https://mlsanity.com/paper/2602.03678/) (2026)

## Abstract (arXiv)

System logs are some of the most important information for the maintenance of software systems, which have become larger and more complex in recent years. The goal of log-based anomaly detection is to automatically detect system anomalies by analyzing the large number of logs generated in a short period of time, which is a critical challenge in the real world. Previous studies have used a log parser to extract templates from unstructured log data and detect anomalies on the basis of patterns of the template occurrences. These methods have limitations for logs with unknown templates. Furthermore, since most log anomalies are known to be point anomalies rather than contextual anomalies, detection methods based on occurrence patterns can cause unnecessary delays in detection. In this paper, we propose LogELECTRA, a new log anomaly detection model that analyzes a single line of log messages more deeply on the basis of self-supervised anomaly detection. LogELECTRA specializes in detecting log anomalies as point anomalies by applying ELECTRA, a natural language processing model, to analyze the semantics of a single line of log messages. LogELECTRA outperformed existing state-of-the-art methods in experiments on the public benchmark log datasets BGL, Sprit, and Thunderbird.