Work

Hybrid Cybersecurity for Asymmetric Threats: Intrusion Detection and SCADA System Protection Innovations

Year: 2025

Type: article

Abstract: Supervisory control and data acquisition (SCADA) systems are vulnerable to cyberattacks; hence, cybersecurity is a major concern. Hybrid methodologies using advanced machine learning (ML) may increase intrusion detection and system security. The intrusion detection algorithms have little adaptability, high false-positive rates for novel threats, and restricted feature extraction. SCADA systems are subject to sophisticated attacks. This study’s hybrid autoencoder-hybrid ResNet–long short-term memory (LSTM) (HAE–HRL) architecture includes deep feature extraction, anomaly detection, and sequential analysis. This framework uses these three methods to improve threat detection. AI can scan massive amounts of data and find patterns humans and traditional systems miss. The hybrid approach gives defenders an unequal edge. Autoencoders identify anomalies, convolutional neural networks (CNNs) extract features, and hybrid ResNet–LSTM learns temporal patterns. Cyber risks are correctly classified using this method. With SCADA security and intrusion detection, the model may considerably enhance network abnormality and hostile activity detection. According to experimental tests, HAE–HRL reduces false positives and improves detection accuracy, making it a robust cybersecurity solution.

Source: Symmetry

DOI: 10.3390/sym17040616

Authors: Abdulmohsen Almalawi, Shabbir Hassan, Adil Fahad, Arshad Iqbal, Asif Irshad Khan

Institutions: King Abdulaziz University, Aligarh Muslim University, Al Baha University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: King Abdulaziz University