SVM-based Dynamic Risk Recognition and Complex Risk Assessment

DOI

10.2991/ic3me-15.2015.161How to use a DOI?

Keywords

Dynamic risk recognition; SVM; Risk factors; Complex risk; Quantified assessment

Abstract

Risk assessment is a critical step for the robust operation of an information system. We incorporate machine learning and statistical theory together in risk recognition and evaluation to accommodate the dynamic and complex characters of information systems. first, SVM classifier is employed to recognize dynamic risk; then risk factor is defined for very single risk based on historical experiences; further, a complex risk assessment model is proposed to quantify risk to capital loss, which provide an intuitive way for user to understand the severity of risks . Experiments show that our method is feasible and effective in practical application environments.

Copyright

© 2015, 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/).

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TY  - CONF
AU  - Xue Liu
AU  - Wenjing Qi
AU  - Weihua Yuan
PY  - 2015/08
DA  - 2015/08
TI  - SVM-based Dynamic Risk Recognition and Complex Risk Assessment
BT  - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
PB  - Atlantis Press
SP  - 842
EP  - 846
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.161
DO  - 10.2991/ic3me-15.2015.161
ID  - Liu2015/08
ER  -