Hybrid data balancing with MLP probabilities-based categorical boosting model for robust intrusion detection system in IoT environment

The rapid expansion of the Internet of Things (IoT) has led to an estimated 29.3 billion connected devices by 2030, generating over 79.4 zettabytes of data annually. However, IoT networks remain highly vulnerable, with nearly 57% of IoT devices susceptible to cyber threats, including Denial-of-Service (DoS) and data spoofing attacks. Existing Intrusion Detection Systems (IDS) often suffer from class imbalance, leading to biased models and reduced detection accuracy for minority attack classes. To address these challenges, a novel Data-Balanced Machine Learning IDS (DBML-IDS) is proposed, integrating data preprocessing, Support Vector Machine (SVM) Weights-based Synthetic Minority Over-sampling Technique (SVWS) for improved data balancing, and a Multi-Layer Perceptron (MLP) Probabilities-based Categorical Boosting (MLPP-CB) classifier. The CICIoT2023 dataset, consisting of two classes (Normal and Attack), is used for evaluation. The proposed DBML-IDS framework ensures optimal feature distribution, mitigates overfitting, and enhances generalization for real-world IoT threat detection. Experimental results demonstrate that DBML-IDS achieves a superior classification performance with accuracy, precision, recall, and an F1-score of 0.9973, outperforming existing IDS models. These findings highlight the effectiveness of the proposed methodology in securing IoT environments against emerging cyber threats.