• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권11호
• /
• pp.4246-4267
• /
• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• International Journal of Internet, Broadcasting and Communication
• /
• 제4권2호
• /
• pp.12-16
• /
• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• 인터넷정보학회논문지
• /
• 제11권1호
• /
• pp.71-76
• /
• 2010

본 연구는 U-러닝 시스템에서 SCORM을 적용하기 위한 방안을 제시하고자 한다. 이는 기존의 SCORM기반의 학습객체 인터페이스 환경인 RTE의 API Instance와 U-러닝을 연결하기 위해 API 브로커를 제시하였다. API 브로커에서는 요구포트와 응답포트를 통해 SCORM과 U-러닝 서버사이의 서비스를 핸들링한다. 또한 각 서비스들의 원활한 운용을 위하여 API 브로커 내에 학습 콘텐츠 서비스 버퍼를 두었다.

• 한국콘텐츠학회논문지
• /
• 제6권3호
• /
• pp.102-107
• /
• 2006

인터넷 원격교육이 보편화되어 가고 있는 현시점에서 인터넷 원격교육의 교육적 효과에 대한 검증은 매우 필요한 연구 대상이라고 할 수 있다. 면대면 교육이 아닌 인터넷 원격교육의 문제점에 대한 분석은 교육적 효과에 대한 검증에서 반드시 거쳐야하는 중요성을 내포하고 있다. 본 연구는 인터넷 원격교육에 참가한 606명의 학생들을 대상으로 한 설문을 바탕으로 인터넷 원격교육의 학습자 입장에서의 문제점에 대한 분석을 실시하였다. 문제점을 환경적 문제, 학생자신의 문제, 교수설계 문제, 운영상의 문제의 4가지 카테고리로 구분하고 카테고리별 문제점들을 도출하여 설문을 통해 문제의 심각성을 분석하였다. 분석 결과는 오늘날의 인터넷 원격교육에 시사하는 점들을 찾을 수 있었다. 또한 각 문제들의 성별, 직업유무, 나이에 따른 차이 유무를 통계적 방법론을 통하여 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권3호
• /
• pp.755-778
• /
• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• 디지털융복합연구
• /
• 제5권2호
• /
• pp.123-134
• /
• 2007

The purpose of this study is to design and implementation of project learning site by using XML. The development of the Internet site for project learning was planned as per preparation, development and test/application stages. The research shows that the elements used for the development of the Internet site for project learning are to give learners motivation, specification of learning goals, reminiscence of preceding knowledge, positive participation in teaching activities, learning-guide feedback, evaluation, reinforcement and correction. It is expected that many teachers apply this model to their classes and show realistic results to motivate their students.

• 과학교육연구지
• /
• 제41권2호
• /
• pp.248-266
• /
• 2017

본 연구는 초등학교 6학년 수학수업에서 수업인터넷을 기반으로 한 협력학습의 다양한 수업사례를 제시하는데 그 목적이 있다. 이를 위해 수업인터넷 기반을 위한 수업환경의 설계 방법을 안내하고 다양한 수업방법에 대한 사례를 제시하였다. 본 연구는 D 광역시의 초등학교 6학년 한 개 반을 대상으로 2016년 3월부터 11월까지 약 9개월간 수업인터넷 기반으로 한 협력학습을 실시하여 대표적인 수업사례 유형을 정리하였다. 그 유형으로는 수업인터넷을 기반으로 한 교실 내 협력학습, 교실-교실 간 원격협력학습, 실시간 참여수업, 프로젝트 협력학습, 가상현실을 이용한 협력학습 등으로 구분할 수 있었으며, 다른 지역의 학생들과 동시간대에 협력학습을 실시할 수 있거나 Youtube를 이용한 학부모 및 교사 참관수업이 가능하다는 점이 기존의 협력학습과 차이가 있다는 것을 알 수 있었다. 이러한 수업사례는 교실 안에서 이루어지는 전통적인 수학수업을 벗어날 수 있는 사례로 제시할 수 있다는 점에서 미래교실에서의 스마트 기기와 인터넷을 활용한 수업방법에 대한 시사점을 제공할 수 있을 것이다.

• 인터넷정보학회논문지
• /
• 제7권6호
• /
• pp.131-141
• /
• 2006

많은 연구에서 E-Learning 시스템에서 학습효과를 높이기 위하여 문항난이도가 적용되었다. 그러나 보다 정확한 난이도 산출을 위해서는 문항교정난이도가 고려되어야 한다. 또한 학습자가 스스로 학습을 계획하고 진행하는 자기 주도적 학습과정이 지원되어야 한다. 따라서 본 연구에서는 문항교정난이도를 이용한 자기 주도적 E-Learning 시스템을 개발하였다. 또한 시스템 개발의 효율성을 위하여 컴포넌트 기반 개발방법으로 구현 및 합성하였다. 본 연구의 적용결과 보다 정확한 문항교정난이도를 학습자에게 지원할 수 있었고, 컴포넌트를 기반으로 합성된 자기 주도적 E-Learning 시스템이 효과적으로 운용됨을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권10호
• /
• pp.2650-2662
• /
• 2012

Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample's label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.

• International Journal of Internet, Broadcasting and Communication
• /
• 제16권2호
• /
• pp.127-135
• /
• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.