Enhancing SDN anomaly detection: a hybrid deep learning model with SCA-TSO optimization

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dc.contributor.authorAlhilo, Ahmed Mohanad Jaber
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2024-07-18T11:36:00Z
dc.date.available2024-07-18T11:36:00Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalıen_US
dc.description.abstractThe paper explores the evolving landscape of network security, in Software Defined Networking (SDN) highlighting the challenges faced by security measures as networks transition to software-based control. SDN revolutionizes Internet technology by simplifying network management and boosting capabilities through the OpenFlow protocol. It also brings forth security vulnerabilities. To address this we present a hybrid Intrusion Detection System (IDS) tailored for SDN environments leveraging a state of the art dataset optimized for SDN security analysis along with machine learning and deep learning approaches. This comprehensive research incorporates data preprocessing, feature engineering and advanced model development techniques to combat the intricacies of cyber threats in SDN settings. Our approach merges feature from the sine cosine algorithm (SCA) and tuna swarm optimization (TSO) to optimize the fusion of Long Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN). By capturing both spatial aspects of network traffic dynamics our model excels at detecting and categorizing cyber threats, including zero-day attacks. Thorough evaluation includes analysis using confusion matrices ROC curves and classification reports to assess the model’s ability to differentiate between attack types and normal network behavior. Our research indicates that improving network security using software defined methods can be achieved by implementing learning and machine learning strategies paving the way, for more reliable and effective network administration solutions.en_US
dc.identifier.citationAlhilo, A. M. J., Koyuncu, H. (2024). Enhancing SDN anomaly detection: a hybrid deep learning model with SCA-TSO optimization. International Journal of Advanced Computer Science and Applications, 15(5), 514-522. 10.14569/IJACSA.2024.0150551en_US
dc.identifier.endpage522en_US
dc.identifier.issn2158-107X
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85197104001
dc.identifier.scopusqualityQ3
dc.identifier.startpage514en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4760
dc.identifier.volume15en_US
dc.identifier.wosWOS:001315627600051
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlhilo, Ahmed Mohanad Jaber
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherScience and Information Organizationen_US
dc.relation.ispartofInternational Journal of Advanced Computer Science and Applications
dc.relation.isversionof10.14569/IJACSA.2024.0150551en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectLSTMen_US
dc.subjectSCAen_US
dc.subjectSDNen_US
dc.subjectTSOen_US
dc.titleEnhancing SDN anomaly detection: a hybrid deep learning model with SCA-TSO optimization
dc.typeArticle

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