Big data network security index correlation measure based on the fusion of modified two order cone programming model

Abstract

The existing technology can not meet the needs of large data network security index measurement in the era of big data. New theories and methods need to be explored to support the application of large data. In this paper, an improved second-order cone programming model (MTOCPM) is used as the data expression vector. The power spectral density feature of large data is extracted from a large number of noisy and fuzzy data. The second-order cone programming model of large data information flow is constructed by rough concept lattice method. Combining with the finite convergence of the second-order cone programming model algorithm, the reliability of each clustering sample is measured, and the accurate search of data clustering centre is realised. Experiments show that the method based on MTOCPM fusion can not only greatly reduce the communication cost between network nodes, but also improve the global measurement accuracy by about 10%.