# ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling

arXiv: https://arxiv.org/abs/2602.03678 · HTML: https://mlsanity.com/paper/2602.03678/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## 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)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://mlsanity.com/paper/2202.04301/) (2022)
- [Deep Learning for Anomaly Detection in Log Data: A Survey](https://mlsanity.com/paper/2207.03820/) (2022)
- [A Taxonomy of Anomalies in Log Data](https://mlsanity.com/paper/2111.13462/) (2021)
- [LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs](https://mlsanity.com/paper/2402.10397/) (2024)

## Abstract (arXiv)

Log files record computational events that reflect system state and behavior, making them a primary source of operational insights in modern computer systems. Automated anomaly detection on logs is therefore critical, yet most established methods rely on log parsers that collapse messages into discrete templates, discarding variable values and semantic content. We propose ContraLog, a parser-free and self-supervised method that reframes log anomaly detection as predicting continuous message embeddings rather than discrete template IDs. ContraLog combines a message encoder that produces rich embeddings for individual log messages with a sequence encoder to model temporal dependencies within sequences. The model is trained with a combination of masked language modeling and contrastive learning to predict masked message embeddings based on the surrounding context. Experiments on the HDFS, BGL, and Thunderbird benchmark datasets empirically demonstrate effectiveness on complex datasets with diverse log messages. Additionally, we find that message embeddings generated by ContraLog carry meaningful information and are predictive of anomalies even without sequence context. These results highlight embedding-level prediction as an approach for log anomaly detection, with potential applicability to other event sequences.