The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les journaux système enregistrent les états du système et les événements importants à divers points critiques pour aider à déboguer les problèmes de performances et les échecs. Par conséquent, la détection rapide et précise du journal système est cruciale pour la sécurité et la stabilité du système. Dans cet article, nous proposons un nouveau modèle de réseau neuronal basé sur l'attention, qui permettrait d'apprendre des modèles de journaux à partir d'une exécution normale. Concrètement, notre modèle adopte un module GRU avec un mécanisme d'attention pour extraire les corrélations et modèles complets et complexes intégrés dans une séquence d'entrées de journal. Les résultats expérimentaux démontrent que notre approche proposée est efficace et permet d'obtenir de meilleures performances que les méthodes conventionnelles.
Yixi XIE
Information Engineering University
Lixin JI
Information Engineering University
Xiaotao CHENG
Information Engineering University
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Yixi XIE, Lixin JI, Xiaotao CHENG, "An Attention-Based GRU Network for Anomaly Detection from System Logs" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 8, pp. 1916-1919, August 2020, doi: 10.1587/transinf.2020EDL8016.
Abstract: System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8016/_p
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@ARTICLE{e103-d_8_1916,
author={Yixi XIE, Lixin JI, Xiaotao CHENG, },
journal={IEICE TRANSACTIONS on Information},
title={An Attention-Based GRU Network for Anomaly Detection from System Logs},
year={2020},
volume={E103-D},
number={8},
pages={1916-1919},
abstract={System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.},
keywords={},
doi={10.1587/transinf.2020EDL8016},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - An Attention-Based GRU Network for Anomaly Detection from System Logs
T2 - IEICE TRANSACTIONS on Information
SP - 1916
EP - 1919
AU - Yixi XIE
AU - Lixin JI
AU - Xiaotao CHENG
PY - 2020
DO - 10.1587/transinf.2020EDL8016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2020
AB - System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.
ER -