Faced with the demand for real-time "big data" processing, the existing financial risk early warning systems are generally difficult to identify hidden risks in massive data information quickly and accurately. This paper introduces a complex event processing technology (CEP), proposes a method of real-time monitoring for "big data", establishes a monitoring model of unusual transactions, designs and realizes a system based on this model. The model contains data acquisition and encapsulation module, custom rules modeling module and results display module. Using real data of a security company to test the system, the results show that, it can identify hidden risks in unusual transactions accurately, and the speed of processing is improved significantly comparing with the system which is based on traditional database analysis method.
Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.
Universal steganalysis include feature extraction and steganalyzer design. Most universal steganalysis use Support Vector Machine (SVM) as steganalyzer. However, most SVM-based universal steganalysis are not to be very much effective at lower embedding rates. The reason why selective SVMs ensemble improve the generalization ability was analyzed, and an algorithm to select a part of individual SVMs according to their difference to build the ensemble classifier was proposed, which based on the selected ensemble theory-Many could be better than all. In this paper, the selective SVMs ensemble algorithm was used to construct a strong steganalyzer to improve the performance of steganographic detection. The twenty five experiments on the benchmark with 2000 different types of images show that: for popular steganography methods, and under different conditions of embedding rate, the average detection rate of proposed steganalysis method outperforms the maximum average detection rate for the steganalysis method based on single SVM with improving by 3.05%-12.05%; and for the steganalysis method based on BaggingSVM with improving by 0.2%-1.3%.
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