# AI based Log Analyser: A Practical Approach

arXiv: https://arxiv.org/abs/2203.10960 · HTML: https://mlsanity.com/paper/2203.10960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [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)

## Abstract (arXiv)

The analysis of logs is a vital activity undertaken for fault or cyber incident detection, investigation and technical forensics analysis for system and cyber resilience. The potential application of AI algorithms for Log analysis could augment such complex and laborious tasks. However, such solution has its constraints the heterogeneity of log sources and limited to no labels for training a classifier. When such labels become available, the need for the classifier to be updated. This practice-based research seeks to address these challenges with the use of Transformer construct to train a new model with only normal log entries. Log augmentation through multiple forms of perturbation is applied as a form of self-supervised training for feature learning. The model is further finetuned using a form of reinforcement learning with a limited set of label samples to mimic real-world situation with the availability of labels. The experimental results of our model construct show promise with comparative evaluation measurements paving the way for future practical applications.