Proactive, Correlation Based Anomaly Detection at the Edge

Abstract

Data management at the edge of the network is a significant research subject. Devices being active at the Internet of Things (IoT) can collect data and transfer them to a set of edge nodes for further processing. There, various activities can be realized. Among them, of great importance it is the detection of anomalies in the incoming data and their preparation to be the subject of advanced processing tasks. In this paper, we propose an ensemble scheme for data anomalies detection and elaborate on the use of an extended sliding window approach. We differentiate from the state of the art solutions and argue on the concept of potential anomalies confirming their presence by incorporating more data into our decision mechanism. The performance of the proposed scheme is evaluated by a set of experimental scenarios being also exposed by numerical results. © 2021 IEEE.