Log-based anomaly detection with deep learning

Author:

Le Van-Hoang1,Zhang Hongyu1

Affiliation:

1. The University of Newcastle, NSW, Australia

Funder

Australian Research Council?s Discovery Projects

Publisher

ACM

Reference55 articles.

1. 2021. Implementation of PLELog . Retrieved August 27, 2021 from https://github.com/YangLin-George/PLELog 2021. Implementation of PLELog. Retrieved August 27, 2021 from https://github.com/YangLin-George/PLELog

2. 2021. A large collection of system log datasets for AI-powered log analytics . Retrieved August 31, 2021 from https://github.com/logpai/loghub 2021. A large collection of system log datasets for AI-powered log analytics. Retrieved August 31, 2021 from https://github.com/logpai/loghub

3. 2021. Log Anomaly Detection Toolkit . Retrieved August 27, 2021 from https://github.com/donglee-afar/logdeep 2021. Log Anomaly Detection Toolkit. Retrieved August 27, 2021 from https://github.com/donglee-afar/logdeep

4. 2021. A Pytorch implementation of DeepLog . Retrieved August 21, 2021 from https://github.com/wuyifan18/DeepLog 2021. A Pytorch implementation of DeepLog. Retrieved August 21, 2021 from https://github.com/wuyifan18/DeepLog

5. 2021. A toolkit for automated log parsing . Retrieved August 31, 2021 from https://github.com/logpai/logparser 2021. A toolkit for automated log parsing. Retrieved August 31, 2021 from https://github.com/logpai/logparser

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3. Impact of log parsing on deep learning-based anomaly detection;Empirical Software Engineering;2024-08-17

4. LogETA: Time-aware cross-system log-based anomaly detection with inter-class boundary optimization;Future Generation Computer Systems;2024-08

5. ContexLog: Non-Parsing Log Anomaly Detection With All Information Preservation and Enhanced Contextual Representation;IEEE Transactions on Network and Service Management;2024-08

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