Enhanced CNN-LSTM deep learning for SCADA IDS featuring hurst parameter self-similarity

• Main Authors: Balla, Asaad, Habaebi, Mohamed Hadi, Elsheikh, Elfatih Abdelrahman Ahmed, Islam, Md Rafiqul, Mohamed Suliman, Fakher Eldin, Mubarak, Sinil
• Format: Article
• Language: English; English; English
• Published: IEEE 2024
• Subjects: TK7885 Computer engineering
• Online Access: http://irep.iium.edu.my/120262/1/120262_Enhanced%20CNN-LSTM.pdf; http://irep.iium.edu.my/120262/2/120262_Enhanced%20CNN-LSTM_SCOPUS.pdf; http://irep.iium.edu.my/120262/9/120262_Enhanced%20CNN-LSTM_WOS.pdf; http://irep.iium.edu.my/120262/; https://ieeexplore.ieee.org/document/10382525

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....