Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
Cyber-physical systems (CPS) combine computational and physical elements to enable effective and intelligent control of several applications. However, the increasing connectivity and complexity of CPS introduce new security challenges, making intrusion detection a critical aspect for maintaining the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10156784/ |
| _version_ | 1797783632039903232 |
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| author | Laila Almutairi Ravuri Daniel Shaik Khasimbee E. Laxmi Lydia Srijana Acharya Hyun-Il Kim |
| author_facet | Laila Almutairi Ravuri Daniel Shaik Khasimbee E. Laxmi Lydia Srijana Acharya Hyun-Il Kim |
| author_sort | Laila Almutairi |
| collection | DOAJ |
| description | Cyber-physical systems (CPS) combine computational and physical elements to enable effective and intelligent control of several applications. However, the increasing connectivity and complexity of CPS introduce new security challenges, making intrusion detection a critical aspect for maintaining the integrity and reliability of these systems. The rise in artificial intelligence (AI) techniques assists in addressing security problems related to CPS environments. Therefore, this study proposes a Quantum Dwarf Mongoose Optimization with Ensemble Deep Learning Based Intrusion Detection (QDMO-EDLID) technique in the CPS environment. The presented QDMO-EDLID technique aims to recognize the presence of intrusions by the feature selection (FS) and ensemble learning process. For feature subset selection purposes, the QDMO-EDLID technique employs the QDMO algorithm. Moreover, an ensemble of Convolution Residual Networks (CRN), Deep Belief Networks (DBN), and Deep Autoencoder (DAE) models are applied for the intrusion classification process. The experimental outcome of the QDMO-EDLID technique was tested employing benchmark intrusion databases. The simulation results highlighted the improved efficiency of the QDMO-EDLID approach concerning different performance measures. |
| first_indexed | 2024-03-13T00:28:38Z |
| format | Article |
| id | doaj.art-083bfc5089b245bfbd28ef5ef0eeedf3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:28:38Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-083bfc5089b245bfbd28ef5ef0eeedf32023-07-10T23:00:14ZengIEEEIEEE Access2169-35362023-01-0111668286683710.1109/ACCESS.2023.328789610156784Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical SystemsLaila Almutairi0Ravuri Daniel1Shaik Khasimbee2E. Laxmi Lydia3https://orcid.org/0000-0003-1751-481XSrijana Acharya4https://orcid.org/0000-0002-0724-8936Hyun-Il Kim5https://orcid.org/0000-0002-4018-4540Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majma’ah, Saudi ArabiaDepartment of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, IndiaDepartment of CSE, Aditya College of Engineering, Surampalem, IndiaDepartment of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, IndiaDepartment of Convergence Science, Kongju National University, Chungcheongnam-do, South KoreaDepartment of Convergence Science, Kongju National University, Chungcheongnam-do, South KoreaCyber-physical systems (CPS) combine computational and physical elements to enable effective and intelligent control of several applications. However, the increasing connectivity and complexity of CPS introduce new security challenges, making intrusion detection a critical aspect for maintaining the integrity and reliability of these systems. The rise in artificial intelligence (AI) techniques assists in addressing security problems related to CPS environments. Therefore, this study proposes a Quantum Dwarf Mongoose Optimization with Ensemble Deep Learning Based Intrusion Detection (QDMO-EDLID) technique in the CPS environment. The presented QDMO-EDLID technique aims to recognize the presence of intrusions by the feature selection (FS) and ensemble learning process. For feature subset selection purposes, the QDMO-EDLID technique employs the QDMO algorithm. Moreover, an ensemble of Convolution Residual Networks (CRN), Deep Belief Networks (DBN), and Deep Autoencoder (DAE) models are applied for the intrusion classification process. The experimental outcome of the QDMO-EDLID technique was tested employing benchmark intrusion databases. The simulation results highlighted the improved efficiency of the QDMO-EDLID approach concerning different performance measures.https://ieeexplore.ieee.org/document/10156784/Cyber-physical systemdeep learningfeature selectionintrusion detectionensemble learning |
| spellingShingle | Laila Almutairi Ravuri Daniel Shaik Khasimbee E. Laxmi Lydia Srijana Acharya Hyun-Il Kim Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems IEEE Access Cyber-physical system deep learning feature selection intrusion detection ensemble learning |
| title | Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems |
| title_full | Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems |
| title_fullStr | Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems |
| title_full_unstemmed | Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems |
| title_short | Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems |
| title_sort | quantum dwarf mongoose optimization with ensemble deep learning based intrusion detection in cyber physical systems |
| topic | Cyber-physical system deep learning feature selection intrusion detection ensemble learning |
| url | https://ieeexplore.ieee.org/document/10156784/ |
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