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This study proposes the developing and implementing of the high-definition level triple streaming hybrid security camera that can output three types of video-signals, which are high-definition serial digital interface, extended serial digital interface, and analog signals. In this study, to develop the high-definition level triple streaming hybrid security camera the hardware and firmware software was designed and implemented. The hardware was implemented using image sensors MN34229PL from Panasonic, main processors EN778/EN331 from Eyenix, Auto Iris functions KA909A from Fairchild, and Zoom/Focus controls A3901SEJTR-T from Microsystems Inc. In addition, to evaluate the performance test of the developed camera in this study, we requested for the performance evaluation at the broadcasting and communication convergence testing by the department of Telecommunication Technology Association, Korea. The results of the performance tests indicated that the performances of the product developed in this study were found to be excellent than the other commercial products with regard to the signal-to-noise ratio, the minimum intensity of illumination, and power consumption. The product designed in this study is expected to be widely utilized in the high-resolution security camera market.

Advances in information and communications technology (ICT) are improving daily convenience and productivity, but new malware threats continue to surge. This paper proposes a malware detection system using various machine learning algorithms and portable executable (PE) Header file static analysis method for malware code, which has recently changed in various forms. Methods/Statistical analysis: This paper proposes a malware detection method that quickly and accurately detects new malware using static file analysis and stacking methods. Furthermore, using information from PE headers extracted through static analysis can detect malware without executing real malware. The features of the pe_packer used in the proposed research method were most efficient in experiments because the extracted data were processed in various ways and applied to machine learning models. So, we chose pe_packer information as the shape data to be used for the stacking model. Detection models are implemented based on additive techniques rather than single models to detect with high accuracy. Findings: The proposed detection system can quickly and accurately classify malware or ordinary files. Moreover, experiments showed that the proposed method has a 95.2% malware detection rate and outperforms existing single model-based detection systems. Improvements/Applications: The proposed research method applies to detecting large new strains of malware.