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A Secure Intrusion Detection system in cyberphysical systems Using a Parameter-Tuned Deep-Stacked Autoencoder

Computers, Materials & Continua 2021年第9期68卷 3915-3929页

作者： Nojood O.Aljehane College of Computer Science and Information Technology University of TabukTabukSaudi Arabia

Cyber physical systems(CPSs)are a networked system of cyber(computation,communication)and physical(sensors,actuators)elements that interact in a feedback loop with the assistance of human ***,CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart *** utilization of CPSs,however,poses many threats,which may be of major significance for *** security issues in CPSs represent a global issue;therefore,developing a robust,secure,and effective CPS is currently a hot research *** resolve this issue,an intrusion detection system(IDS)can be designed to protect *** the IDS detects an anomaly,it instantly takes the necessary actions to avoid harming the *** this study,we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning(DL),called PT-DSAE,for the IDS in *** proposed model involves preprocessing,feature extraction,parameter tuning,and ***,data preprocessing takes place to eliminate the noise present in the ***,a DL-based DSAE model is applied to detect anomalies in the *** addition,hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE,such as the number of hidden layers,batch size,epoch count,and learning *** assess the experimental outcomes of the PT-DSAE model,a series of experiments were performed using data from a sensor-based ***,a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE *** experimental results obtained verified the superior performance on the applied data over the compared methods.

关键词： cyberphysical system intrusion detection system autoencoder cybersecurity

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