Publication detail

Analyzing anomalies in industrial networks: A data-driven approach to enhance security in manufacturing processes

KUCHAŘ, K. FUJDIAK, R.

Original Title

Analyzing anomalies in industrial networks: A data-driven approach to enhance security in manufacturing processes

Type

journal article in Web of Science

Language

English

Original Abstract

Industrial networks are adapted to their specific requirements, especially in terms of industrial processes. To ensure sufficient security in these networks, it is necessary to set and use security policies that complement government regulations, recommendations, and relevant security standards. This paper aims to provide an in-depth analysis of the anomalies occurring within the networks and propose a structure for collecting valuable data from the experimental site based on dividing anomalies into three main categories: security, operational, and service anomalies (and regular traffic recognition). We present a proof-of-concept solution/design aggregating data in industrial networks for advanced anomaly classification. Multiple data sources such as industrial communication, sensor data (additional sensors controlling device behavior), and HW status data are used as data sources. A total of three scenarios (using a physical testbed) were implemented, where we achieved an accuracy of 0.8541/0.9972 in advanced anomaly classification.

Keywords

Neural networks (NN); Anomaly; Anomaly classification; Sensory data; Cybersecurity; Industrial Control System (ICS); Operational Technology (OT)

Authors

KUCHAŘ, K.; FUJDIAK, R.

Released

28. 2. 2025

ISBN

0167-4048

Periodical

COMPUTERS & SECURITY

Year of study

153

Number

June 2025

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

15

Pages count

15

URL

https://www.sciencedirect.com/science/article/pii/S0167404825000847?utm_campaign=STMJ_219742_AUTH_SERV_PA&utm_medium=email&utm_acid=277298152&SIS_ID=&dgcid=STMJ_219742_AUTH_SERV_PA&CMX_ID=&utm_in=DM547969&utm_source=AC_

BibTex

@article{BUT197746,
  author="Karel {Kuchař} and Radek {Fujdiak}",
  title="Analyzing anomalies in industrial networks: A data-driven approach to enhance security in manufacturing processes",
  journal="COMPUTERS & SECURITY",
  year="2025",
  volume="153",
  number="June 2025",
  pages="1--15",
  doi="10.1016/j.cose.2025.104395",
  issn="0167-4048",
  url="https://www.sciencedirect.com/science/article/pii/S0167404825000847?utm_campaign=STMJ_219742_AUTH_SERV_PA&utm_medium=email&utm_acid=277298152&SIS_ID=&dgcid=STMJ_219742_AUTH_SERV_PA&CMX_ID=&utm_in=DM547969&utm_source=AC_"
}