CBM-IDS: An Advanced Hybrid Deep Learning Model for DDoS Attack Detection in IoT Networks

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dc.contributor.authorKaramollaoglu, Hamdullah
dc.contributor.authorYucedag, Ibrahim
dc.contributor.authorDogru, Ibrahim Alper
dc.contributor.authorToklu, Sinan
dc.contributor.authorAtacak, Ismail
dc.date.accessioned2026-07-01T11:39:16Z
dc.date.available2026-07-01T11:39:16Z
dc.date.issued2026
dc.departmentDüzce Üniversitesi
dc.description.abstractThe 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.
dc.identifier.doi10.3897/jucs.146099
dc.identifier.endpage132
dc.identifier.issn0948-695X
dc.identifier.issn0948-6968
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105033921942
dc.identifier.scopusqualityQ3
dc.identifier.startpage108
dc.identifier.urihttps://doi.org/10.3897/jucs.146099
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23203
dc.identifier.volume32
dc.identifier.wosWOS:001683342300005
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGraz Univ Technolgoy, Inst Information Systems Computer Media-Iicm
dc.relation.ispartofJournal of Universal Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subject[Keyword Not Available]
dc.titleCBM-IDS: An Advanced Hybrid Deep Learning Model for DDoS Attack Detection in IoT Networks
dc.typeArticle

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