Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Enhanced CNN-LSTM Deep Learning for SCADA IDS Featuring Hurst Parameter Self-Similarity

Supervisory Control and Data Acquisition (SCADA) systems are crucial for modern industrial processes and securing them against increasing cyber threats is a significant challenge. This study presents an advanced method for bolstering SCADA security by employing a modified hybrid deep learning model....

Full description

Bibliographic Details
Main Authors: Asaad Balla, Mohamed Hadi Habaebi, Elfatih A. A. Elsheikh, Md. Rafiqul Islam, Fakher Eldin Mohamed Suliman, Sinil Mubarak
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
intrusion detection system
supervisory control and data acquisition
self-similarity
Hurst parameter
binary classification
Online Access:https://ieeexplore.ieee.org/document/10382525/

Internet

https://ieeexplore.ieee.org/document/10382525/

Similar Items