Detail publikace

Syslog Anomaly Detection Using Supervised Machine Learning Models

YOUNESIAN, E. SIKLOSI, M. KHATIB, N. HOSEK, J.

Originální název

Syslog Anomaly Detection Using Supervised Machine Learning Models

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Supervised Machine Learning, Anomaly Detection

Autoři

YOUNESIAN, E.; SIKLOSI, M.; KHATIB, N.; HOSEK, J.;

Vydáno

5. 10. 2021

Nakladatel

IEEE

ISBN

978-1-6654-0219-4

Kniha

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

Strany od

78

Strany do

84

Strany počet

7

BibTex

@inproceedings{BUT173144,
  author="YOUNESIAN, E. and SIKLOSI, M. and KHATIB, N. and HOSEK, J.",
  title="Syslog Anomaly Detection Using Supervised Machine Learning Models",
  booktitle="2021 13th Congress on Ultra Modern Telecommunications and Control Systems and Workshops",
  year="2021",
  pages="78--84",
  publisher="IEEE",
  doi="10.1109/ICUMT54235.2021.9631564",
  isbn="978-1-6654-0219-4"
}