Log Message Anomaly Detection with Oversampling

Farzad, Amir; Gulliver, T. Aaron

doi:10.5121/ijaia.2020.11405

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Imbalanced data sets are another challenge that is specifically addressed by some approaches. In particular, authors suggest to use sampling techniques as well as context-aware embedding methods as possible solutions [35], [45], [55], [62], [66].…”

Section: Discussionmentioning

confidence: 99%

“…Another popular choice for RNNs are Gated Recurrent Units (GRU) that simplify the cell architecture as they only rely on update and reset gates. One of the main benefits of GRUs is that they are computationally more efficient than LSTM RNNs, which is a relevant aspect for use cases focusing on edge devices [21], [34], [35], [37], [53], [56], [62], [68], [69].…”

Section: B Deep Learning Techniquesmentioning

confidence: 99%

“…They actually consist of two separate components that compete with each other: a generator that produces new data that resembles the input data, and a discriminator that estimates the probability that some data stems from the input data, which is used to improve the generator. Existing approaches use different models to construct GANs, including LSTM RNNs [41], [66], CNNs in combination with GRUs [35], and Transformers [1], [76].…”

Section: B Deep Learning Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Anomaly Detection in Log Data: A Survey

Landauer¹,

Onder²,

Skopik³

et al. 2022

Preprint

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Automatic log file analysis enables early detection of relevant incidents such as system failures. In particular, selflearning anomaly detection techniques capture patterns in log data and subsequently report unexpected log event occurrences to system operators without the need to provide or manually model anomalous scenarios in advance. Recently, an increasing number of approaches leveraging deep learning neural networks for this purpose have been presented. These approaches have demonstrated superior detection performance in comparison to conventional machine learning techniques and simultaneously resolve issues with unstable data formats. However, there exist many different architectures for deep learning and it is nontrivial to encode raw and unstructured log data to be analyzed by neural networks. We therefore carry out a systematic literature review that provides an overview of deployed models, data pre-processing mechanisms, anomaly detection techniques, and evaluations. The survey does not quantitatively compare existing approaches but instead aims to help readers understand relevant aspects of different model architectures and emphasizes open issues for future work.

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