CBM-IDS: An Advanced Hybrid Deep Learning Model for DDoS Attack Detection in IoT Networks
JOURNAL OF UNIVERSAL COMPUTER SCIENCE, cilt.32, sa.1, ss.108-132, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 32 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.3897/jucs.146099
- Dergi Adı: JOURNAL OF UNIVERSAL COMPUTER SCIENCE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, Directory of Open Access Journals
- Sayfa Sayıları: ss.108-132
- Gazi Üniversitesi Adresli: Evet
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.