• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.4043-4060
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• 2017

Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.449-456
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• 2019

SMART home is one of the most popular applications of Internet-of-Things (IoT) technologies, which is expanding in terms of range of applications. SMART home technology provides convenience at home by connecting household appliances to a single network, control, and management. However, many general home appliances do not support the network functions yet; hence, enjoying such convenient technology could be difficult, and it could be expensive in the beginning to build the framework. In addition, even though products with SMART home technologies are purchased, the control systems could differ from device to device. Thus, in this paper, we propose a SMART home framework, called an S-mote that can operate all the IoT functions in a single application by adding an infrared or radio frequency module to general home appliances. The proposed framework is analyzed using four types of performance tests by five evaluators. The results of the experiment show that the SMART home environment was implemented successfully and that it functions appropriately, without any operational issues, with various home appliances, including the latest IoT devices, and even those equipped with an infrared or radio frequency module.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.163-168
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• 2021

Unlike in the past, profit-making companies use design thinking to solve their customers' essential needs rather than just solving problems in a conventional way. Design syncing is a creative strategy to solve problems by using designers' senses and methods in the process of design. Design thinking is a new way of thinking for designers to design their own ways and solve problems. In other words, design thinking can solve complex problems in a new and creative way. In order to realize the necessary functions, an aggregation of relevant elements in accordance with the new law can create an innovative design. Additionally, if a convergence system is applied that is organized and regularly functional to accomplish the functions of different kinds, design-thinking outcomes can make the necessary functions more specific. Through our research, we would like to explore the differential features of design and discuss the direction of design for consumer needs through a case analysis of design thinking with creative convergence system. We would like to propose competitive design product development and creative design thinking through case analysis such as products and systems with design thinking applied. We hope that this research will help businesses and individuals who make design thinking a problem.

• 인터넷정보학회논문지
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• 제23권5호
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• pp.17-23
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• 2022

Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1446-1465
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• 2022

Diversified SaaS applications allow users more choices to use, according to their own preferences. However, the diversification of SaaS applications also makes it impossible for users to choose the best one. Furthermore, users can't take advantage of the functionality between SaaS applications. In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment. The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework. Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques. We present the theoretical basis of implementing the high-performance message-processing function using Kafka. The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function. The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.

• 대한기계학회논문집A
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• 제28권7호
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• pp.961-969
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• 2004

Most manufacturing companies are trying to develop a competitive product by increasing the quality, shortening time to market and reducing the cost of a product. Collaborators related to the development of a new product want to confirm geometric forms and dimensions during the design process, as well as to verify dimensional errors of a product during the fabrication process. Objective of this paper is the development of a collaborative design and dimension verification system on the Internet. STEP files obtained from the design process are used for the design and dimension verification. Functions of the design and dimension verification modules are constructed over the ActiveX control using the visual C/sup ++/ and OpenGL. By using mark up functions over the Internet, collaborators check geometries, interferences, dimensional errors, human factors and form errors, as well as share their design ideas and opinions with XML rapidly and remotely. The usefulness of the developed system is confirmed through case studies.

• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.17-23
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• 2017

커넥티드카는 연결성이 주요 요소로서 항시 LTE나 5G 무선이동통신에 의한 인터넷 접속이 가능하여 인포테인먼트 기능 구현에 있어 새로운 개념의 스마트PC 하드웨어 및 서버의 음성인식 엔진을 이용한 디지털 가상비서의 소프트웨어 설계 방법을 활용할 수 있다. 본 논문에서는 음성인식 기술에 기반한 커넥티드카 인포테인먼트 시스템 구현을 위하여 스마트 오토PC의 하드웨어 및 GENIVI 플랫폼에서의 소프트웨어 구조와 필요한 기능 등을 제시한다. 또한 객체지향 분석 방법을 이용하여 사용자 요구사항 분석, 플로우차트 및 화면 설계를 보여줌으로 효과적인 커넥티드카 인포테인먼트 소프트웨어 분석 및 설계 방법을 제시하고자 한다.

• 인터넷정보학회논문지
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• 제22권6호
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.1-15
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• 2023

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning. To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics. As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments. The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress. In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed. On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students. It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.75-81
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• 2022

주차 공간의 부족함을 획기적으로 해결할 수 있는 자율주행 자동차는 심층 강화 학습을 통해 큰 발전을 이루고 있다. 심층 강화 학습에는 활성화 함수가 사용되는데, 그동안 다양한 활성화 함수가 제안되어 왔으나 적용 환경에 따라 그 성능 편차가 심했다. 따라서 환경에 따라 최적의 활성화 함수를 찾는 것이 효과적인 학습을 위해 중요하다. 본 논문은 자율주행 자동차가 주차를 학습하기 위해 심층 강화 학습을 사용할 때 어떤 활성화 함수를 사용하는 것이 가장 효과적인지 비교 평가하기 위해 강화 학습에 주로 사용되는 12개의 함수를 분석하였다. 이를 위해 성능 평가 환경을 구축하고 각 활성화 함수의 평균 보상을 성공률, 에피소드 길이, 자동차 속도와 비교하였다. 그 결과 가장 높은 보상은 GELU를 사용한 경우였고, ELU는 가장 낮았다. 두 활성화 함수의 보상 차이는 35.2%였다.