Publication detail

Identification of industrial devices based on payload

POSPÍŠIL, O. FUJDIAK, R.

Original Title

Identification of industrial devices based on payload

Type

conference paper

Language

English

Original Abstract

Identification of industrial devices based on their behavior in network communication is important from a cybersecurity perspective in two areas: attack prevention and digital forensics. In both areas, device identification falls under asset management or asset tracking. Due to the impact of active scanning on these networks, particularly in terms of latency, it is important to use passive scanning in industrial networks. For passive identification, statistical learning algorithms are nowadays the most appropriate. The aim of this paper is to demonstrate the potential for passive identification of PLC devices using statistical learning based on network communication, specifically the payload of the packet. Individual statistical parameters from 15 minutes of traffic based on payload entropy were used to create the features. Three scenarios were performed and the XGBoost algorithm was used for evaluation. In the best scenario, the model achieved an accuracy score of 83% to identify individual devices.

Keywords

PLC, OT, Identification, ICS, ML, XGBoost

Authors

POSPÍŠIL, O.; FUJDIAK, R.

Released

30. 7. 2024

Publisher

Association for Computing Machinery

Location

New York, NY, USA

ISBN

979-8-4007-1718-5

Book

ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security

Pages from

1

Pages to

9

Pages count

9

URL

https://dl.acm.org/doi/10.1145/3664476.3670462

BibTex

@inproceedings{BUT189222,
  author="Ondřej {Pospíšil} and Radek {Fujdiak}",
  title="Identification of industrial devices based on payload",
  booktitle="ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security",
  year="2024",
  pages="1--9",
  publisher="Association for Computing Machinery",
  address="New York, NY, USA",
  doi="10.1145/3664476.3670462",
  isbn="979-8-4007-1718-5",
  url="https://dl.acm.org/doi/10.1145/3664476.3670462"
}