loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Lu An ; An-Jie Tu ; Xiaotong Liu and Rama Akkiraju

Affiliation: IBM Watson, 555 Bailey Ave, San Jose, U.S.A.

Keyword(s): AI for IT Operations, Log Anomaly Detection, Online Statistical Learning, Error Entity Extraction, Continuous Model Updating.

Abstract: Anomaly detection from logs is a fundamental Information Technology Operations (ITOps) management task. It aims to detect anomalous system behaviours and find signals that can provide clues to the reasons and the anatomy of a system’s failure. Applying advanced, explainable Artificial Intelligence (AI) models throughout the entire ITOps is critical to confidently assess, diagnose and resolve such system failures. In this paper, we describe a new online log anomaly detection algorithm which helps significantly reduce the time-to-value of Log Anomaly Detection. This algorithm is able to continuously update the Log Anomaly Detection model at run-time and automatically avoid potential biased model caused by contaminated log data. The methods described here have shown 60% improvement on average F1-scores from experiments for multiple datasets comparing to the existing method in the product pipeline, which demonstrates the efficacy of our proposed methods.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.149.214.32

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
An, L.; Tu, A.; Liu, X. and Akkiraju, R. (2022). Real-time Statistical Log Anomaly Detection with Continuous AIOps Learning. In Proceedings of the 12th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-570-8; ISSN 2184-5042, SciTePress, pages 223-230. DOI: 10.5220/0011069200003200

@conference{closer22,
author={Lu An. and An{-}Jie Tu. and Xiaotong Liu. and Rama Akkiraju.},
title={Real-time Statistical Log Anomaly Detection with Continuous AIOps Learning},
booktitle={Proceedings of the 12th International Conference on Cloud Computing and Services Science - CLOSER},
year={2022},
pages={223-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011069200003200},
isbn={978-989-758-570-8},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Cloud Computing and Services Science - CLOSER
TI - Real-time Statistical Log Anomaly Detection with Continuous AIOps Learning
SN - 978-989-758-570-8
IS - 2184-5042
AU - An, L.
AU - Tu, A.
AU - Liu, X.
AU - Akkiraju, R.
PY - 2022
SP - 223
EP - 230
DO - 10.5220/0011069200003200
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic