The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks.

Internet of Things (IoT) networks are integral to modern life due to their pervasive connectivity and automation capabilities. Intrusion Detection Systems (IDS) are crucial in IoT ecosystems to countermeasure attacks that can compromise devices and disrupt essential services. Their role is vital in maintaining the integrity, confidentiality, and availability of data within these networks. The effectiveness of these security systems is fundamentally dependent on the robustness of learning algorithms and the quality of the datasets utilized. Class imbalance is a common challenge in real-world datasets, where certain classes are represented by significantly fewer instances compared to others. This paper studies the impact of balancing the BoT-IoT dataset on the performance of Machine Learning (ML) based IDSs using three algorithms: K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Support Vector Machine (SVM). To address the class imbalance problem, we apply two resampling techniques, random upsampling and Synthetic Minority Over-sampling Technique (SMOTE). We evaluate the efficacy of the models through various performance metrics, including accuracy, precision, recall, and F1-score. The findings of our experimental work prove that balanced datasets lead to more dependable and robust IDSs that are capable of handling real-world data with varied class distributions.