Enhancing IIOT security with machine learning and deep learning for intrusion detection

Basit Öğe Kaydı
dc.authorid0000-0001-8066-2175en_US
dc.authorid0000-0003-4511-7694en_US
dc.contributor.authorAwad, Omer Fawzi
dc.contributor.authorHazim, Laytha Rafea
dc.contributor.authorJasim, Abdulrahman Ahmed
dc.contributor.authorAta, Oğuz
dc.date.accessioned2024-07-12T11:08:13Z
dc.date.available2024-07-12T11:08:13Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well -optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin -the -middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.en_US
dc.identifier.citationAwad, O.F., Hazim, L.R., Jasim, A.A., Ata, O. (2024). Enhancing IIOT security with machine learning and deep learning for intrusion detection. Malaysian Journal of Computer Science, 37(2), 139-153.en_US
dc.identifier.endpage153en_US
dc.identifier.issn0127-9084
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85196637101
dc.identifier.scopusqualityQ3
dc.identifier.startpage139en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4741
dc.identifier.volume37en_US
dc.identifier.wosWOS:001250232200003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAwad, Omer Fawzi
dc.institutionauthorHazim, Laytha Rafea
dc.institutionauthorJasim, Abdulrahman Ahmed
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherUniversity Of Malayaen_US
dc.relation.ispartofMalaysian Journal of Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectIIoTen_US
dc.subjectCybersecurityen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectMachine Learningen_US
dc.subjectKNIMEen_US
dc.titleEnhancing IIOT security with machine learning and deep learning for intrusion detection
dc.typeArticle

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