Anomaly Detection in Log Streams based on Time-Contextual Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00381387" target="_blank" >RIV/68407700:21240/25:00381387 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-96-0576-7_2" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-96-0576-7_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-0576-7_2" target="_blank" >10.1007/978-981-96-0576-7_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly Detection in Log Streams based on Time-Contextual Models

Popis výsledku v původním jazyce

Organisations today heavily rely on complex software systems integrated through multiple layers of middleware. This complexity leads to substantial generation of operational data of structured and semi-structured formats which is recorded in log files. The workload of the system fluctuates according to specific periods of the day which impacts the amount and quality of data generated in log files. In this paper, we propose a new log anomaly detection approach that leverages a collection of smaller models designed to capture workload fluctuations over specific time intervals. We demonstrate its effectiveness in detecting anomalies within log streams. Our evaluation uses log data from servers in a production environment, handling a complex back-end system that processes hundreds of requests per second. We show that our method outperforms traditional and widely used anomaly detection methods in data streams in the context of dynamic and time-sensitive workload scenarios.

Název v anglickém jazyce

Anomaly Detection in Log Streams based on Time-Contextual Models

Popis výsledku anglicky

Organisations today heavily rely on complex software systems integrated through multiple layers of middleware. This complexity leads to substantial generation of operational data of structured and semi-structured formats which is recorded in log files. The workload of the system fluctuates according to specific periods of the day which impacts the amount and quality of data generated in log files. In this paper, we propose a new log anomaly detection approach that leverages a collection of smaller models designed to capture workload fluctuations over specific time intervals. We demonstrate its effectiveness in detecting anomalies within log streams. Our evaluation uses log data from servers in a production environment, handling a complex back-end system that processes hundreds of requests per second. We show that our method outperforms traditional and widely used anomaly detection methods in data streams in the context of dynamic and time-sensitive workload scenarios.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Web Information Systems Engineering – WISE 2024

ISBN

978-981-96-0575-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

19-29

Název nakladatele

Springer Nature Singapore Pte Ltd.

Místo vydání

—

Místo konání akce

Doha

Datum konání akce

2. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001534861200002