A Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies

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dc.contributor.authorAl-Dulaimi, Reem Talal Abdulhameed
dc.contributor.authorTürkben, Ayça Kurnaz
dc.date.accessioned2025-07-03T07:21:27Z
dc.date.available2025-07-03T07:21:27Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractIn the realm of cybersecurity, detecting Distributed Denial of Service (DDoS) attacks with high accuracy is a critical task. Traditional machine learning models often fall short in handling the complexity and high dimensionality of network traffic data. This study proposes a hybrid framework leveraging symmetry in feature distribution, network behavior, and model optimization for anomaly detection. A Tree Convolutional Neural Network (Tree-CNN) captures hierarchical symmetrical dependencies, while a deep autoencoder preserves latent symmetrical structures, reducing noise for better classification. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is proposed to optimize the parameters of the system and achieve better performance. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is introduced to maintain a symmetrical balance between exploration and exploitation, optimizing the autoencoder, Tree-CNN, and classification thresholds. Validation using three datasets—UNSW-NB15, CIC-IDS 2017, and CIC-IDS 2018—demonstrates the framework’s superiority. The model achieves 96.02% accuracy on UNSW-NB15, 99.99% on CIC-IDS 2017, and 99.96% on CIC-IDS 2018, with near-perfect precision and recall. Despite a slightly higher computational cost, the symmetrically optimized framework ensures high efficiency and superior detection, making it ideal for real-time complex networks. These findings emphasize the critical role of symmetrical network patterns and feature selection strategies for enhancing intrusion detection performance.
dc.identifier.citationAl-Dulaimi, R. T. A., & Türkben, A. K. (2025). A Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies. Symmetry, 17(3), 421.
dc.identifier.doi10.3390/sym17030421
dc.identifier.issn2073-8994
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105001115730
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5788
dc.identifier.volume17
dc.identifier.wosWOS:001453786600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Dulaimi, Reem Talal Abdulhameed
dc.institutionauthorTürkben, Ayça Kurnaz
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofSymmetry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDDoS attacks
dc.subjectensemble learning
dc.subjectfalse positives
dc.subjecthierarchical features
dc.subjectnetwork traffic analysis
dc.subjectparticle swarm optimization
dc.subjectpelican optimization
dc.titleA Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies
dc.typeArticle

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