Behaviour-Based Trust Assessment for the Internet of Things Systems Using Multi-Classifier Ensemble Learning and Dempster-Shafer Fusion

With the rapid proliferation of the Internet of Things (IoT), robust trust management has become imperative to ensure security in these IoT systems. Prior machine learning approaches to IoT trust management have exhibited suboptimal performance, failing to capture the dynamic behaviour and complex discriminative features of IoT devices. To address these challenges, we design an evocative trust management scheme for user’s authentication based on Dempster Shafer's evidence theory, which can persuade the normal activities of IoT device systems. We establish a set of discriminating features to predict the trustworthiness of a network node by assessing its observed behaviours. These behaviours being assessed encompass several characteristics such as throughput, delay, jitter, and network latency. As such, nodes that demonstrate elevated data transmission rates, and have anomalous traffic patterns could potentially be categorised as untrustworthy. In addition, the existence of persistently high latency will impede trust prediction algorithms, this will consequently alter the overall behaviour of the node, ultimately affecting the trustworthiness of the entire network. We also design a framework for the fusion of evidence based on the belief degree and reputation-based evidence to avoid misclassification resulting in evidence conflicting. The resultant fusion outcomes are transformed into category labels which serve as the prediction outcome of the multi-classifier ensemble scheme. We evaluate our proposed scheme with and without the best discriminative features on performance metrics including accuracy, precision, recall, F1-score, detection rate, and false alarm rate. Comprehensive experiments on a transformed UNSW NB15 dataset demonstrate the better performance of our proposed framework, especially in the application of evidence conflicting.