Model based IoT security framework using multiclass adaptive boosting with SMOTE

Abstract

Security and threats are growing immensely due to the higher usage of internet of things applications in all aspects. Due to imbalanced nature of IoT security data, the designing of model‐based anomaly detection in IoT network poses a challenge for machine learning model as most of the machine learning model assumes the equal number of samples for each class. Approximately, 2.79% of IoT network profiles are of anomaly types which impose severe imbalance where there are three samples in the anomaly types for hundreds of samples in the majority normal class. This results in poor predictive performance for identification of anomaly type, which is essentially a problem because the anomaly type is more sensitive than the normal activity type. This work proposes a multiclass adaptive boosting ensemble learning‐based model with the synthetic minority oversampling technique for prediction of an anomaly in IoT network. The proposed approaches are simulated with DS2OS data and the performance is compared with other machine learning approaches. The evaluation metrics such as sensitivity, F1‐score, and receiver operating characteristic‐AUC imply the efficiency of the proposed approach in handling the imbalanced nature of the data and found efficient to identify both anomaly types and normal activity.

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

“…Cheng et al [128] utilized a semi-supervised hierarchical stacking model for anomaly detection in IoT communication. Dash et al [129] proposed a multiclass adaptive boosting ensemble learning-based model with the synthetic minority oversampling technique for anomaly detection in IoT network. Du and Zhang [51] applied a two-level selective ensemble learning algorithm for handling imbalanced datasets.…”