• Journal of Communications and Networks
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• 제12권1호
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• pp.86-94
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• 2010

In this paper, we present a decentralized algorithm to find minimum cost quality of service (QoS) flow subgraphs in network coded multicast schemes. The main objective is to find minimum cost subgraphs that also satisfy user-specified QoS constraints, specifically with respect to rate and delay demands. We consider networks with multiple multicast sessions. Although earlier network coding algorithms in this area have demonstrated performance improvements in terms of QoS parameters, the proposed QoS network coding approach provides a framework that guarantees QoS constraints are actually met over the network.

• 정보보호학회논문지
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• 제17권2호
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• pp.61-72
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• 2007

With the increasing growth of Internet and wireless IP networks, Multimedia systems need to be envisaged as information resources where users can access anywhere and anytime. However, efficient services in these multimedia systems are open and challenging research problem due to user mobility, limited resources in wireless devices and expensive radio bandwidth. To implement multimedia services over heterogeneous network, the IP header compression scheme can be used for saving bandwidth. In this paper, we present an efficient solution for header compression, which is modified form of ECRTP. It shows an architectural framework adopting modified ECRTP when IP tunneling network using GRE over IPSec is implemented. We have conducted simulations in order to analyze the effects of different header compression techniques while delivering real-time services to the wireless access network through secured IP Network. The impacts on performance have been investigated through a series of experiments.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.63-66
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• 2021

The technological advancement in computer vision has made system like grab-and-go grocery a reality. Now all the shoppers have to do now is to walk in grab the items and go out without having to wait in the long queues. This paper presents an intelligent retail environment system that is capable of monitoring and tracking customer's activity during shopping based on their interaction with the shelf. It aims to develop a system that is low cost, easy to mount and exhibit adequate performance in real environment.

• 방송공학회논문지
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• 제13권1호
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• pp.92-98
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• 2008

네트워크 데이터 모델링은 침입 탐지 시스템의 성능 평가, 네트워크 모니터링, 네트워크 데이터 분석 기법 연구에 있어서 반드시 필요한 연구이다. 네트워크 데이터의 모델링에는 반드시 네트워크의 실제 데이터를 분석하고, 분석된 데이터를 이용하여 효과적으로 데이터를 구성하여야만, 실제 네트워크 데이터의 충분한 정보를 모델링 된 데이터에 반영할 수 있다. 본 연구에서는 대규모의 네트워크 데이터에서 실제 네트워크에서 사용 가능한 모든 성분에 대해 수량화하였으며, 수량화 된 데이터를 통계적 분석방법을 통하여 모델링 데이터에서 가장 효과적인 분류 기준으로 작용할 수 있는 성분을 분석하였다.

• 정보보호학회논문지
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• 제29권4호
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• pp.859-866
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• 2019

최근의 사이버 보안 위협은 특정 표적을 대상으로 하는 특징이 있으며 보안을 강화시키기 위한 지속적인 노력에도 불구하고 APT 공격에 의한 피해 사례는 계속 발생하고 있다. 인터넷망과 업무망이 분리된 망분리 환경은 외부 정보의 유입을 봉쇄시킬 수 있으나 업무의 효율성과 생산성을 위해서는 현실적으로 외부 정보의 유입을 모두 통제할 수는 없다. 이에 망연계 시스템 등 보안 정책을 강화시키고 파일 내부에 포함된 불필요한 데이터를 제거할 수 있도록 CDR 기술이 적용된 솔루션을 도입하더라도 여전히 보안 위협에 노출되어 있다. 본 연구는 망분리 환경에서 망간 파일을 전송할 때 파일의 형식을 변환하여 전송함으로써 문서삽입형 악성코드의 보안 위협을 방지하는 방안을 제안한다. 또한 포렌식 준비도를 고려하여 문서파일이 원활한 사고대응을 위한 정보를 보관할 수 있는 기능을 포함하여 망 분리 환경에서 활용할 수 있는 시스템을 제안한다.

• 한국산업정보학회논문지
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• 제11권3호
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• pp.34-39
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• 2006

트래픽 패턴-맵(Pattern-Map)은 전체/세부 도메인별 보안 상황을 근원지/목적지 IP 주소 범위로 이루어진 그리드 상에 표현하여 관리자에게 네트워크 보안상황을 실시간으로 인지시키는 도구이다. 각각의 그리드는 근원지-목적지간의 연결을 의미하며, 최다 점유를 차지하는 트래픽의 포트를 식별력을 갖는 색으로 표현한다. 이상 트래픽 현상의 검출은 가로 및 세로 열에 나타난 동일 색의 막대그래프(포트)의 개수와 그것의 합에 따라 결정되며, 그 결과로 선택된 세로 열과 가로 열을 활성화시켜 관리자에게 그 현상을 인지시킨다. 일반적으로 인터넷 웜이 발생할 경우에는 특정 근원지 열이 활성화되고, DDoS와 같은 현상은 목적지 열이 활성화되는 특징이 있다

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• 한국정보보호학회:학술대회논문집
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• 한국정보보호학회 1997년도 종합학술발표회논문집
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• pp.265-273
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• 1997

Intranet is an enterprise network using Internet protocols as communication standard and HTML as content standard. The Internet is like a house built on information water. It has a lot of strong points as a future enterprise network. However, companies wish to have confidence in its functional and economic effectiveness and security before adopting it. The security issue especially is a problem to solve inevitably. Enterprises will hold back to adopt Intranet unless there are enough security counter plans and countermeasures against vulnerabilities of Intranet(it is the wise decision !). Nevertheless the researches related to Intranet has been concentrated on techniques for building it. In this paper, we focus the security aspect of Intranet. Intranet security must be considered on the whole from structure design to users' services. We propose a security-based Intranet structure and security management system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1818-1832
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• 2022

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.