Publication detail

Syslog Anomaly Detection Using Supervised Machine Learning Models

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

Original Title

Syslog Anomaly Detection Using Supervised Machine Learning Models

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Supervised Machine Learning, Anomaly Detection

Authors

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

Released

5. 10. 2021

Publisher

IEEE

ISBN

978-1-6654-0219-4

Book

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

Pages from

78

Pages to

84

Pages count

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"
}