iSPmA: A Novel IOT Security Event Perception Model based on Autonomic Computing

DOI

10.2991/iccsae-15.2016.28How to use a DOI?

Keywords

Internet of Things, Autonomic Computing, Principal Component Analysis, Feature Extraction

Abstract

According to the high dimension of security event feature and the difficulty of security event autonomic perception in the age of big data, a novel Internet of Things ( IOT , for short) security event perception model based on Autonomic Computing and Principal Component Analysis (PCA, for short) is proposed, including element extraction, element understanding and event prediction. In which, to improve the real-time performance of element understanding, PCA is adopted to map the initial high dimensional feature to a set of new unrelated synthesized feature, and back propagation neural network (BP neural network, for short) is used to fuse the synthesized feature after reduction. The experimental result show that, feature reduction by PCA can greatly reduce the input dimension of fusion engine, efficiently cuts down the learning time of BP neural network, and improves the accuracy of event perception.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Ruijuan Zheng
AU  - Tenghao Li
AU  - Mingchuan Zhang
AU  - Qingtao Wu
AU  - Zhengchao Ma
AU  - Wangyang Wei
AU  - Chunlei Yang
PY  - 2016/02
DA  - 2016/02
TI  - iSPmA: A Novel IOT Security Event Perception Model based on Autonomic Computing
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 137
EP  - 145
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.28
DO  - 10.2991/iccsae-15.2016.28
ID  - Zheng2016/02
ER  -