Architecture of the parallel Big Data Processing system for security monitoring of Internet of Things networks

Internet-of-Things networks are applied in many areas of people life now. A cornerstone in a issue of a possibility of further distribution and use of these networks is the aspect of security support. However, the features of these networks complicate the use of traditional means and systems of computer protection in them. One of such features is the need to analyze very large volumes of data, heterogeneous by the nature, in real time and with the minimum computing expenses. Taking into account the features of computational capabilities of Internet-of-Things networks the architecture of the system for parallel big data processing based on the data processing technology named as Complex Event Processing and the parallel computing platform Hadoop is offered. The issues directly connected to the architecture of the system and with implementation of its principal components are considered. These components are: data collection component, data storage component, data normalization and analysis component, and data visualization component. An interconnection between components is provided by means of the Hadoop Distributed File System that is a basis for creation of the distributed data storage. The data collection component organizes the distributed data acquisition and their storage in the data storage component. The data normalization and analysis component transforms data to a uniform format and processes them by means of correlation rules. The data visualization component presents data in a graphical form more suitable for further perception by the operator. The results of the experimental evaluation of the system performance confirming a conclusion about its high performance are discussed.