Machine Learning-Based Detection of Anomalies, Intrusions, and Threats in Industrial Control Systems
Industrial Control Systems (ICS) are critical to the efficient operation of essential sectors such as manufacturing, energy, and water management. However, their increasing integration with IT systems exposes them to sophisticated cyberattacks, particularly lateral attacks targeting Programmable Log...
Saved in:
Main Authors: | Denis Benka, Dusan Horvath, Lukas Spendla, Gabriel Gaspar, Maximilian Stremy |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10843706/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on intrusion detection of industrial control system
by: Ying-xu LAI, et al.
Published: (2017-02-01) -
ContractGuard:defend Ethereum smart contract with embedded intrusion detection
by: Gansen ZHAO, et al.
Published: (2020-04-01) -
Overview of anomaly detection techniques for industrial Internet of things
by: Haili SUN, et al.
Published: (2022-03-01) -
Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT
by: Sepehr Sharifi, et al.
Published: (2024-02-01) -
Research on a dynamic self-learning efficient intrusion detection model
by: YANG Wu1, et al.
Published: (2007-01-01)