Enhanced IoT Security: Multi-Stage Intrusion Detection with Hybrid Residual Networks
Keywords:
IoT Security, Intrusion Detection System (IDS), Hybrid Deep Learning, ResNet34, ResNet50, Feature Extraction, Principal Component Analysis (PCA), Data Preprocessing, Anomaly Detection, Feature Fusion, Attention Mechanism, Network Security, Deep Neural NetworksAbstract
With the rapid expansion of Internet of Things (IoT) networks, intrusion detection has become increasingly critical due to the heterogeneous nature and vulnerability of connected devices. This study proposes a hybrid IoT Intrusion Detection System (IDS) modeled as a multi-stage framework, encompassing data preprocessing, feature extraction, and deep learning-based classification. Missing data are addressed using mean imputation, and categorical and numerical features are standardized through label encoding and Z-score normalization to ensure uniform scaling. Principal Component Analysis (PCA) is applied for dimensionality reduction, preserving essential variance while reducing redundancy. The classification function is implemented using several state-of-the-art deep learning architectures, including MobileNet, Inception, VGG16, VGG19, DenseNet, GoogLeNet, AlexNet, ResNet34, and ResNet50. A hybrid model combining ResNet34 and ResNet50 with feature-level fusion and attention mechanisms is employed to enhance learning depth and feature representation. The system is trained using cross-entropy loss and evaluated on training and testing subsets. Results demonstrate improved detection accuracy, reduced false alarms, and effective classification of normal and anomalous IoT network activities.Downloads
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