Abstract

The paper offers a new approach to Big Data processing for security monitoring of mobile Internet of things elements based on machine learning and its implementation using parallel algorithms. The architecture of security monitoring system is considered. It specifies several machine learning mechanisms intended for solving classification tasks. The classifier operation results are exposed to plurality voting, weight voting and soft voting. The experimental assessment of performance and accuracy of the offered methods is made.