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LogBERT: Log Anomaly Detection via BERT
TL;DR: This paper proposes LogBERT, a self-supervised framework for log anomaly detection based on Bidirectional Encoder Representations from Transformers (BERT), which is able to detect anomalies where the underlying patterns deviate from normal log sequences.
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Abstract: Detecting anomalous events in online computer systems is crucial to protect the systems from malicious attacks or malfunctions. System logs, which record detailed information of computational events, are widely used for system status analysis. In this paper, we propose LogBERT, a self-supervised framework for log anomaly detection based on Bidirectional Encoder Representations from Transformers (BERT). LogBERT learns the patterns of normal log sequences by two novel self-supervised training tasks and is able to detect anomalies where the underlying patterns deviate from normal log sequences. The experimental results on three log datasets show that LogBERT outperforms state-of-the-art approaches for anomaly detection.
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Citations
Log-based Anomaly Detection with Deep Learning: How Far Are We?
Van-Hoang Le,Hongyu Zhang +1 more
- 09 Feb 2022
TL;DR: An in-depth analysis of five state-of-the-art deep learning-based models for detecting system anomalies on four public log datasets, focusing on several aspects of model evaluation, including training data selection, data grouping, class distribution, data noise, and early detection ability.
Deep Learning for Anomaly Detection in Log Data: A Survey
TL;DR: An overview of deployed models, data pre-processing mechanisms, anomaly detection techniques, and evaluations can be found in this paper , where the authors provide an overview of different model architectures and open issues for future work.
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BERT-Log: Anomaly Detection for System Logs Based on Pre-trained Language Model
Song Chen,Hai Liao +1 more
TL;DR: BERT-Log as discussed by the authors uses a pre-trained language model to learn the semantic representation of normal and anomalous logs, and a fully connected neural network is utilized to fine-tune the BERT model to detect abnormal.
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Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation
Xiao Han,Shuhan Yuan +1 more
- 26 Oct 2021
TL;DR: Li et al. as discussed by the authors proposed a transferable log anomaly detection (LogTAD) framework that leverages the adversarial domain adaptation technique to make log data from different systems have a similar distribution so that the detection model is able to detect anomalies from multiple systems.
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BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
TL;DR: BTAD as mentioned in this paper uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel.
40
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