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Smart city article details
| Title | Leveraging Digital Twins With Hybrid Deep Learning Model For Robust Intrusion Detection System In Smart City Environment |
|---|---|
| ID_Doc | 35068 |
| Authors | Alghanmi N.A. |
| Year | 2025 |
| Published | Fusion: Practice and Applications, 19, 1 |
| DOI | http://dx.doi.org/10.54216/FPA.190116 |
| Abstract | Cyber-physical systems (CPSs) unite the computation with physical methods. Embedded networks and computers observe and handle the physical procedures, generally with feedback encircles whereas physical procedures affect computation and conversely. In the last decade, the prompt growth of network-associated services has formed confidential information on the Internet. However, networks are much inclined to intrusions wherever unapproved consumers try to retrieve confidential data and even disturb the systems. Constructing a proficient network intrusion detection system (IDS) can be essential to avert these attacks. Utilizing digital twin technology enhances the IDS of physical devices in CPSs. IDSs normally utilize machine learning (ML) techniques for categorizing the attacks. However, the features employed for classifications are not appropriate or adequate all the time. Moreover, the amount of intrusions can be significantly lower than the amount of non-intrusions. Therefore, simple techniques may fail to deliver satisfactory performances owing to this class imbalance. In this study, we offer a Metaheuristic-Driven Hybrid Deep Learning Model for Robust Intrusion Detection in Secure Cyber-Physical Systems (MHDLM-RIDCPS) model in Smart City Environment. The proposed MHDLM-RIDCPS technique primarily targets the classification and recognition of intrusions using digital twin technology to enhance security within the CPS. Primarily, the proposed MHDLM-RIDCPS approach utilizes min-max normalization for transforming an input data into a standardized format. To alleviate dimensionality issues, the coyote optimization algorithm (COA) can be executed to select a subset of features. In addition, the modified prairie dog optimizer (mPDO) combined with a convolutional neural network and bi-directional long short-term memory with attention mechanism (AM-CNN+BiLSTM) classifier is exploited for the identification of intrusions. The design of the mPDO system primarily concentrates on the parameter optimizer of the AM-CNN+BiLSTM algorithm and so improves the classifier performances. To determine the greater efficiency of the MHDLM-RIDCPS system, a comprehensive set of simulations can be applied and the performances are tested over distinct aspects. The experimental analysis guaranteed the superior results of the MHDLM-RIDCPS methodology with existing methods. © 2025, American Scientific Publishing Group (ASPG). All rights reserved. |
| Author Keywords | Attack; Cyber-Physical System; Deep Learning; Digital Twins; Feature Selection; Intrusion Detection; Metaheuristic Algorithm; Smart City Environment |
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