Syslog Anomaly Detection Using Supervised Machine Lesrning Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU142085" target="_blank" >RIV/00216305:26220/21:PU142085 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Syslog Anomaly Detection Using Supervised Machine Lesrning Models

Popis výsledku v původním jazyce

Nowadays, detecting anomalies is crucial for managing every network. Massive logs are produced by modern large-scale distributed systems. These logs contain useful information regarding network behavior. Traditionally, developers detect anomalies by complex coded scripts. However, such approach is not efficient for large-scale systems where they generate thousands of logs. Thus, syslog anomalz detection tool has been proposed in this paper by using supervised machine learning models. As a source of dataset for the machine learning models, syslog generator was developed to generate the desired dataset. A comprative study about many supervised methods has been evaluated in this paper using different amount of datasets. The target was to check the impact of enlargement of datasets on the performance of the anomaly detections.

Název v anglickém jazyce

Syslog Anomaly Detection Using Supervised Machine Lesrning Models

Popis výsledku anglicky

Nowadays, detecting anomalies is crucial for managing every network. Massive logs are produced by modern large-scale distributed systems. These logs contain useful information regarding network behavior. Traditionally, developers detect anomalies by complex coded scripts. However, such approach is not efficient for large-scale systems where they generate thousands of logs. Thus, syslog anomalz detection tool has been proposed in this paper by using supervised machine learning models. As a source of dataset for the machine learning models, syslog generator was developed to generate the desired dataset. A comprative study about many supervised methods has been evaluated in this paper using different amount of datasets. The target was to check the impact of enlargement of datasets on the performance of the anomaly detections.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 13th Congress on Ultra Modern Telecommunications and Control Systems and Workshops

ISBN

978-1-6654-0219-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

78-84

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Online

Datum konání akce

25. 10. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

—