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Industrial Intrusion Detection with MLP by Noman Khan is a document available to read on EtoBox.
This research presents a multilayer perceptron-based anomaly detection framework for industrial control systems (ICS), specifically evaluated using the Tennessee Eastman process. The proposed model demonstrates high accuracy (96.97%) and efficiency in detecting cyberattacks, addressing the limitations of existing statistical methods. By integrating advanced deep learning techniques, the study aims to enhance cybersecurity measures in the context of Industry 4.0, where interconnected systems face significant
- Author
- Noman Khan
- Language
- EN