A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder

作     者：Nojood O.Aljehane 

作者机构：College of Computer Science and Information TechnologyUniversity of TabukTabukSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3915-3929页

核心收录：

学科分类：0839[工学-网络空间安全] 08[工学] 

主　　题：Cyberphysical system intrusion detection system autoencoder cybersecurity 

摘      要：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.