A Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies
Yükleniyor...
Dosyalar
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
DDoS attacks, ensemble learning, false positives, hierarchical features, network traffic analysis, particle swarm optimization, pelican optimization
Kaynak
Symmetry
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
17
Sayı
3
Künye
Al-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.
