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dc.contributor.authorMassaoudi, Mohamed
dc.contributor.authorRefaat, Shady S.
dc.contributor.authorAbu-Rub, Haitham
dc.date.accessioned2023-05-31T10:15:01Z
dc.date.available2023-05-31T10:15:01Z
dc.date.issued2022-05-18
dc.identifier.citationMassaoudi , M , Refaat , S S & Abu-Rub , H 2022 , Intrusion Detection Method Based on SMOTE Transformation for Smart Grid Cybersecurity . in 3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings . 3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings , Institute of Electrical and Electronics Engineers (IEEE) , 3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 , Doha , Qatar , 20/03/22 . https://doi.org/10.1109/SGRE53517.2022.9774070
dc.identifier.citationconference
dc.identifier.isbn9781665479080
dc.identifier.otherORCID: /0000-0001-9392-6141/work/136239152
dc.identifier.urihttp://hdl.handle.net/2299/26370
dc.description© 2022 IEEE.
dc.description.abstractReal-Time Intrusion Detection Systems (IDSs) have attracted greater attention for secured and resilient smart grid operations. IDSs are employed to identify unknown cyberattacks and malware from network traffics. In this paper, an efficient model-based machine learning is proposed to detect a variety of cyberattacks. The proposed method enhanced Extremely randomized Trees (ET) classifier based on Synthetic Minority Oversampling Technique (SMOTE) accurately classifies imbalanced IDSs data. The proposed ET-SMOTE uses a virtue of data processing blocks to enable multi-layer network cyber-security assessment in smart grids by acquiring the essential knowledge of attack dynamics. The proposed computing framework provides an accurate multiclass classification of five network traffic categories: denial of service attacks, probing attacks, root to local attacks, user to root attacks, and normal. The experimental results demonstrate the high accuracy of the proposed ET-SMOTE algorithm in detecting various types of cyber threats compared to benchmark models with an accuracy of 99.79% using the NSL-KDD networks data set.en
dc.format.extent6
dc.format.extent655846
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings
dc.relation.ispartofseries3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings
dc.subjectIntrusion detection
dc.subjectmachine learning
dc.subjectmulti-layer cybersecurity
dc.subjectnetwork traffic
dc.subjectsmart grid vulnerability
dc.subjectArtificial Intelligence
dc.subjectComputer Science Applications
dc.subjectEnergy Engineering and Power Technology
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectElectrical and Electronic Engineering
dc.subjectSafety, Risk, Reliability and Quality
dc.subjectControl and Optimization
dc.titleIntrusion Detection Method Based on SMOTE Transformation for Smart Grid Cybersecurityen
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85130872681&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1109/SGRE53517.2022.9774070
rioxxterms.typeOther


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