The adoption of the Internet of Things (IoT) has increased across diverse domains, where home appliances have recently been equipped with sensors for internet connectivity. However, the generation of large network traffic has led to the occurrence of anomalous activities. The conventional methods designed for anomaly detection lack full-spectrum monitoring and lead to false detection, resource wastage, and biased detections in high-dimensional data. Therefore, this research proposes the Hierarchical Attention-enabled Drift-based Tunable Convolutional Neural Network (HADrTCN) model to address the prevailing challenges as well as to provide accurate anomaly detection. Further, the drift-based tunable network automatically adapts to evolving data patterns via dynamically adjusting the parameters to detect the anomaly patterns in IoT networks. In addition, the HADrTCN model is distributed as two parallel functioning layers that promote faster processing as well as improve the scalability in handling larger datasets. Moreover, the Hierarchical Attention (HA) layer is incorporated in each distributed layer, allowing the proposed model to focus on the key discriminative features for detecting the anomaly patterns. Furthermore, the Generative Adversarial Network (GAN) is leveraged to deal with the dataset imbalances through refining the class distributions. Data preprocessing is performed to resolve the inconsistencies as well as to normalize the feature attributes. Experimental results show that the HADrTCN model outperforms the other existing techniques by achieving the high accuracy of 98.13%, indicating the substantial improvement of 1.94% compared to existing HEET, and 1.07% compared to TSMAE on CIC-IDS2017 dataset.
Mohammed et al. (2026) studied this question.