Detecting and explaining anomalies in industrial internet of things systems using an autoencoder

In industrial Internet of Things (IoT) systems, explaining anomalies plays a crucial role in identifying bottlenecks and optimizing processes. This paper proposes an approach to anomaly detection using an autoencoder and its explanation based on the SHAP method. The purpose of the anomaly explanation is to provide a set of data features in industrial IoT systems that most significantly influence anomaly detection. The novelty of this approach lies in its ability to quantify the contribution of individual features for specific data samples and to calculate an average contribution across the dataset, providing a feature importance ranking. The proposed approach is tested on Industrial IoT datasets with varying feature counts and data volumes. The anomaly detection achieves an F-measure of 88-93%, outperforming the comparable methods discussed. The study demonstrates how explainable artificial intelligence can identify the causes of anomalies in both individual samples and datasets as a whole. The theoretical importance of the proposed approach lies in its ability to shed light on the workings of intelligent detection models, enabling the identification of factors influencing their outcomes and uncovering previously unnoticed patterns. In practice, this method enhances security system operators' understanding of ongoing processes, aiding in threat identification and error detection within data.