• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.319-324
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• 2004

The Java language is rapidly being adopted in the Internet. The distributed applications and their application range are being expanded beyond just a programing language and developed Into software applications. A variety of researches are going on with regard to the Java Virtual Machine runtime environment and methods of analyzing the Java class files and utilizing the information for applications. A class file is a converted file that is executable by the Java virtual machine. Analysis on the class file structure and the runtime processes will be convenient in arranging the decompilers and debugging the source programs. This paper is about the runtime process analyzer that presents the runtime processes, including class files, more visually. The content of a class file will be easily accessed and expressed as in a graphic user interface. The information in the class file displayed is divided into Constant_Pool, Class_file, Interface, Field, Method and Attribute with information on method area, operand stack and local variables expressed visually.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.479-484
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• 2002

This paper presents the model that integrates an SDL system and a CORBA system using mapping rules between method and signal for developing embedded systems connected with internet. In order to support communication between the two different systems (SDL and CORBA), it is essential to secure the conversion interface between SDL communication protocol and CORBA communication protocol. In this paper, IDL is adopted for the communication interface and the conversion of the communication protocol between the two systems, and the IDL compiler automatically generates the interface for protocol interoperability. The proposed model adopts middleware on the subpart of the SDL based legacy system, and hence, supports the service on the distributed system, regardless of the environment and location of the server system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.76-84
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• 2009

This paper proposes a SOC platform which is eligible for education and application SOC design. The platform, fully synthesizable and reconfigurable, includes the OpenRISC embedded processor, some basic peripherals such as GPIO, UART, debug interlace, VGA controller and WISHBONE interconnect. The platform uses a set of development environment such as compiler, assembler, debugger and RTOS that is built for HW/SW system debugging and software development. Designed SOC, IPs and Testbenches are described in the Verilog HDL and verified using commercial logic simulator, GNU SW development tool kits and the FPGA. Finally, a multimedia SOC derived from the SOC platform is implemented to ASIC using the Magnachip cell library based on 0.18um 1-poly 6-metal technology.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.1-8
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• 2020

Recently, the number of APT(Advanced Persistent Threats) attack using malware has been increasing, and research is underway to prevent and detect them. While it is important to detect and block attacks before they occur, it is also important to make an effective response through an accurate analysis for attack case and attack type, these respond which can be determined by analyzing the attack group of such attacks. Therefore, this paper propose a framework based on genetic algorithm for analyzing malware and understanding attacker group's features. The framework uses decompiler and disassembler to extract related code in collected malware, and analyzes information related to author through code analysis. Malware has unique characteristics that only it has, which can be said to be features that can identify the author or attacker groups of that malware. So, we select specific features only having attack group among the various features extracted from binary and source code through the authorship clustering method, and apply genetic algorithm to accurate clustering to infer specific features. Also, we find features which based on characteristics each group of malware authors has that can express each group, and create profiles to verify that the group of authors is correctly clustered. In this paper, we do experiment about author classification using genetic algorithm and finding specific features to express author characteristic. In experiment result, we identified an author classification accuracy of 86% and selected features to be used for authorship analysis among the information extracted through genetic algorithm.