CBM-IDS: An Advanced Hybrid Deep Learning Model for DDoS Attack Detection in IoT Networks
Küçük Resim Yok
Tarih
2026
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The rapid expansion of IoT devices has transformed industries while simultaneously introducing critical security vulnerabilities, particularly Distributed Denial-of-Service (DDoS) attacks that exploit the constrained resources of IoT systems. To address this challenge, a novel intrusion detection system (CBM-IDS) is proposed for the effective identification and mitigation of DDoS attacks in IoT environments. A hybrid deep learning framework is employed, integrating Convolutional Neural Networks (CNN) for spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency analysis, and a Multi-Head Attention Mechanism (MHAM) to prioritize critical network traffic patterns. Model robustness is enhanced through Adaptive Synthetic Sampling (ADASYN) and One-Sided Selection (OSS) for class imbalance mitigation, along with dimensionality reduction using an Autoencoder combined with ANOVA F-test-based feature selection. The proposed system is evaluated on the CICDDoS2019 benchmark dataset, achieving a detection accuracy of 99.93%, which demonstrates its efficacy in real-world IoT security applications.
Açıklama
Anahtar Kelimeler
[Keyword Not Available]
Kaynak
Journal of Universal Computer Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
32
Sayı
1
