Log anomaly detection is a key component in the field of artificial
intelligence for IT operations (AIOps). Considering log data of variant
domains, retraining the whole network for unknown domains is inefficient in
real industrial scenarios. However, previous deep models merely focused on
extracting the semantics of log sequences in the same domain, leading to poor
generalization on multi-domain logs. To alleviate this issue, we propose a
unified Transformer-based framework for Log anomaly detection (LogFormer) to
improve the generalization ability across different domains, where we establish
a two-stage process including the pre-training and adapter-based tuning stage.
Specifically, our model is first pre-trained on the source domain to obtain
shared semantic knowledge of log data. Then, we transfer such knowledge to the
target domain via shared parameters. Besides, the Log-Attention module is
proposed to supplement the information ignored by the log-paring. The proposed
method is evaluated on three public and one real-world datasets. Experimental
results on multiple benchmarks demonstrate the effectiveness of our LogFormer
with fewer trainable parameters and lower training costs.
Log anomaly detection is a crucial aspect of artificial intelligence for IT operations, as it allows organizations to identify and address abnormal events in log data. However, existing deep models in this field have primarily focused on extracting the semantics of log sequences within a single domain, which limits their ability to generalize across multiple domains.
In this article, the authors propose a unified Transformer-based framework called LogFormer to address this limitation and improve the generalization ability across different domains. The framework consists of a two-stage process, starting with pre-training on a source domain to obtain shared semantic knowledge of log data. This pre-trained model is then fine-tuned on the target domain using adapter-based tuning, where shared parameters are transferred to leverage the knowledge obtained from the source domain.
One key contribution of LogFormer is the introduction of the Log-Attention module. This module supplements the information ignored by log-pairing, which is a technique commonly used for log analysis. By incorporating the Log-Attention module into the Transformer-based model, LogFormer is able to capture additional information from log data, leading to improved anomaly detection performance.
To evaluate the effectiveness of LogFormer, the authors conducted experiments on three public datasets and one real-world dataset. The experimental results demonstrate that LogFormer outperforms existing methods in terms of both detection accuracy and efficiency. Notably, LogFormer achieves these improvements while utilizing fewer trainable parameters and incurring lower training costs compared to previous approaches.
The multi-disciplinary nature of the concepts presented in this article is worth highlighting. The authors combine techniques from artificial intelligence, particularly deep learning and Transformers, with IT operations and log analysis. By leveraging shared parameters and a two-stage process, LogFormer demonstrates the potential for cross-domain generalization in log anomaly detection tasks.
Moving forward, there are several avenues for further exploration in this field. Firstly, it would be valuable to investigate how LogFormer performs on a broader range of domains beyond those considered in the experiments. Additionally, exploring the use of different pre-training techniques, such as self-supervised learning or unsupervised representation learning, could further enhance the generalization abilities of LogFormer. Furthermore, considering the ever-evolving nature of log data in IT operations, ongoing research should focus on developing techniques that can adapt and update the model’s knowledge to stay relevant in dynamic environments. Overall, LogFormer represents a significant step forward in log anomaly detection, showcasing the potential benefits of multi-domain generalization and offering promising directions for future research.
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