Detecting Anomalies in Device Event Data in the IoT

Irene Cramer, Prakash Govindarajan, Minu Martin, Alexandr Savinov, Arun Shekhawat, Alexander Staerk, Appasamy Thirugnana

2018

Abstract

This paper describes an approach to detecting anomalous behavior of devices by analyzing their event data. Devices from a fleet are supposed to be connected to the Internet by sending log data to the server. The task is to analyze this data by automatically detecting unusual behavioral patterns. Another goal is to provide analysis templates that are easy to customize and that can be applied to many different use cases as well as data sets. For anomaly detection, this log data passes through three stages of processing: feature generation, feature aggregation, and analysis. It has been implemented as a cloud service which exposes its functionality via REST API. The core functions are implemented in a workflow engine which makes it easy to describe these three stages of data processing. The developed cloud service also provides a user interface for visualizing anomalies. The system was tested on several real data sets, such as data generated by autonomous lawn mowers where it produced meaningful results by using the standard template and only little parameters.

Paper Citation

in Harvard Style

Cramer I., Govindarajan P., Martin M., Savinov A., Shekhawat A., Staerk A. and Thirugnana A. (2018). Detecting Anomalies in Device Event Data in the IoT.In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-296-7, pages 52-62. DOI: 10.5220/0006670100520062

in Bibtex Style

@conference{iotbds18,
author={Irene Cramer and Prakash Govindarajan and Minu Martin and Alexandr Savinov and Arun Shekhawat and Alexander Staerk and Appasamy Thirugnana},
title={Detecting Anomalies in Device Event Data in the IoT},
booktitle={Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2018},
pages={52-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006670100520062},
isbn={978-989-758-296-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Detecting Anomalies in Device Event Data in the IoT
SN - 978-989-758-296-7
AU - Cramer I.
AU - Govindarajan P.
AU - Martin M.
AU - Savinov A.
AU - Shekhawat A.
AU - Staerk A.
AU - Thirugnana A.
PY - 2018
SP - 52
EP - 62
DO - 10.5220/0006670100520062