Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks

Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal co...

Full description

Saved in:
Bibliographic Details
Main Authors: Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee
Format: Article
Language:English
Published: Springer 2025-02-01
Series:Discover Internet of Things
Subjects:
Cyber-security
Deep learning
Adversarial attack
Industrial control system
XAI
Intrusion detection
Online Access:https://doi.org/10.1007/s43926-025-00100-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1007/s43926-025-00100-0

Similar Items