African buffalo optimization with deep learning-based intrusion detection in cyber-physical systems

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 25, 2025

Cyber-physical system (CPS) incorporates several computing resources, networking units, interconnected physical processes, and monitoring the development application of system. Interconnection between cyber worlds initiates attacks on security problems, particularly with enhancing complications transmission networks. Despite efforts to combat these analyzing detecting cyber-physical from complex CPS is challenging. Machine learning (ML)-researcher workers implemented based techniques examine systems. A competent network intrusion detection (IDS) essential avoid attacks. Generally, IDS uses ML classify However, features used for classification are not frequently appropriate or adequate. Moreover, number intrusions much lower than that non-intrusions. This research presents an African Buffalo Optimizer Algorithm a Deep Learning Intrusion Detection (ABOADL-IDS) model in environment. The main intention ABOADL-IDS utilize FS optimal DL approach recognition identification procedure. Initially, performs data normalization process. Furthermore, utilizes ABO technique feature selection. stacked deep belief (SDBN) employed identification. To improve SDBN solution, seagull optimization (SGO) hyperparameter assessment accomplished under NSLKDD2015 CICIDS2015 datasets. performance validation illustrated superior accuracy value 99.28% over existing models concerning various measures.

Язык: Английский

Optimal cybersecurity framework for smart water system: Detection, localization and severity assessment

Water Research, Год журнала: 2025, Номер unknown, С. 123517 - 123517

Опубликована: Март 1, 2025

Язык: Английский