期刊
IEEE ACCESS
卷 6, 期 -, 页码 72714-72723出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2881998
关键词
Big Data; machine learning; security monitoring; mobile security; Internet of Things; classifier
资金
- RFBR [16-29-09482, 18-07-01369, 18-07-01488, AAAA-A16-116033110102-5]
- Government of the Russian Federation [08-08]
The paper discusses a new framework combining the possibilities of Big Data processing and machine leaning developed for security monitoring of mobile Internet of Things. The mathematical foundations and the problem statement are considered. The description of the used data set and the architecture of proposed security monitoring framework are provided. The framework specifies several machine learning mechanisms intended for solving classification tasks. The classifier operation results are exposed to plurality voting, weighted voting, and soft voting. The framework performance and accuracy is assessed experimentally.
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