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

• Main Authors: Balla, Asaad, Habaebi, Mohamed Hadi, Elsheikh, Elfatih A. A., Islam, Md Rafiqul, Suliman, F.M., mubarak, Sinil
• Format: Article
• Language: English; English
• Published: IEEE Xplore 2024
• Subjects: TK7885 Computer engineering
• Online Access: http://irep.iium.edu.my/110380/7/110380_Enhanced%20CNN-LSTM%20Deep%20Learning.pdf; http://irep.iium.edu.my/110380/8/110380_Enhanced%20CNN-LSTM%20Deep%20Learning%20_SCOPUS.pdf; http://irep.iium.edu.my/110380/; https://ieeexplore-ieee-org.ezlib.iium.edu.my/document/10382525?source=authoralert

T 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 mo...