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...
Saved in:
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: | |
Online Access: | https://doi.org/10.1007/s43926-025-00100-0 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Threat of Tomorrow: Impacts of Artificial Intelligence-Enhanced Cyber-attacks on International Relations
by: Esra Merve Çalışkan
Published: (2024-12-01) -
Securing cyber-physical robotic systems for enhanced data security and real-time threat mitigation
by: Akashdeep Bhardwaj, et al.
Published: (2025-01-01) -
On the issue of criminal law protection of cyber security of Ukraine in modern conditions
by: Chuvakov O.
Published: (2024-06-01) -
Deep learning for cyber threat detection in IoT networks: A review
by: Alyazia Aldhaheri, et al.
Published: (2024-01-01) -
Cyber Attacks on Commercial Drones: A Review
by: Bruno Branco, et al.
Published: (2025-01-01)