Detail publikace

Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges

KUCHAŘ, K. FUJDIAK, R.

Originální název

Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Industrial networks, due to communication convergence, face a growing exposure to cyber threats, necessitating the need to address a wider range of threats, alongside their detectability and classification. As critical components designed with a strong emphasis on availability, industrial networks require precise classification of anomalies, encompassing not just cyber anomalies but also operational and service disruptions. This paper provides an analysis of these anomalies, categorizing them into three groups based on their impact. The key contribution of this study lies in the strategic distribution of data sources across the Operational Technology (OT) network, facilitating the collection of relevant data for application in Machine Learning (ML) or Neural Network (NN) models. A comprehensive review of current anomaly processing techniques in industrial networks is presented, identifying significant research challenges to advance artificial intelligence methods for anomaly classification in OT environments. Additionally, this work examines common statistical methods for anomaly detection and offers a comparative analysis of prevalent ML and NN techniques.

Klíčová slova

Anomaly types; sensory data; cyber-security; industrial control system (ICS); operational technology (OT); Neural Network (NN)

Autoři

KUCHAŘ, K.; FUJDIAK, R.

Vydáno

12. 12. 2024

Nakladatel

Institute of Electrical and Electronics Engineers Inc.

ISSN

1558-1748

Periodikum

IEEE SENSORS JOURNAL

Ročník

25

Číslo

3

Stát

Spojené státy americké

Strany od

1

Strany do

14

Strany počet

14

URL

https://ieeexplore.ieee.org/document/10797650

BibTex

@article{BUT193646,
  author="Karel {Kuchař} and Radek {Fujdiak}",
  title="Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges",
  journal="IEEE SENSORS JOURNAL",
  year="2024",
  volume="25",
  number="3",
  pages="1--14",
  doi="10.1109/JSEN.2024.3512857",
  issn="1558-1748",
  url="https://ieeexplore.ieee.org/document/10797650"
}