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. 

Intrusion detection, IoT network, deep learning, feature engineering, DDoS attacks