Preprocessing and Framework for Unsupervised Anomaly Detection in IoT: Work on Progress

Published on Oct 1, 2018 in ICEE (International Conference on Electrical Engineering)
· DOI :10.1109/ICECOS.2018.8605231
Kurniabudi3
Estimated H-index: 3
,
Benni Purnama3
Estimated H-index: 3
+ 3 AuthorsRahmat Budiarto13
Estimated H-index: 13
(Al Baha University)
Sources
Abstract
A robust increasing on smart sensors in Internet of Thing (IoT) results huge and heterogenous data and becomes a challenge in data prepocessing and analysis for anomaly detection. The lack of IoT publicly available dataset is one issue in anomaly detection research. To resolve that problem, a testbed topology is proposed in this research. In addition, a high-dimensionality data analysis faces a computational complexity. The purpose of this study is to presents a global framework for anomaly detection in IoT and proposes a distributed preprocessing framework. Unsupervised learning approach has been chosen to reduce dimensionality of IoT data traffic.
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