Syslog Anomaly Detection Using Supervised Machine Lesrning Models

Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Syslog Anomaly Detection Using Supervised Machine Lesrning Models
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2021 13th Congress on Ultra Modern Telecommunications and Control Systems and Workshops
ISBN
978-1-6654-0219-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
78-84
Publisher name
IEEE
Place of publication
neuveden
Event location
Online
Event date
Oct 25, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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