A Novel Approach for Collective Anomaly Detection in Internet of Things

Abstract

Internet of Things (IoT) is a network which connects several devices with the internet to have quick and real-time information transmission, attaining intelligent management. IoT is still in its beginning so it faces a lot of challenges. One huge challenge related to data management is the detection of collective anomalies. A collective anomaly is a set of points which differ as a whole from the rest of the data points. However, the individual points are not considered anomalies. Detection of collective anomalies is of huge importance as collective anomalies usually denote the occurrence of events or certain incidents that should be detected as soon as possible before they cause damage to the surrounding environment. Detection of collective anomalies is considered a complicated process as it involves more than one point rather than only one point as in point anomaly detection process. As a result, in this paper, we propose a novel approach of collective anomaly detection using a variable sized window in IoT. Two techniques were used to detect the collective anomalies, namely: K-Means clustering and isolation forests. The proposed approach was applied on a machine temperature dataset of size around 22,000 and the experiments showed promising results.