• 한국정보통신학회논문지
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• 제20권1호
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• pp.193-199
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• 2016

최근 다양한 분야에서 소셜 네트워크 분석 기법을 통해 개체들간의 상호작용을 분석하는 연구들이 진행되고 있다. 본 논문에서는 한국정보통신학회 논문지의 저자약력에 기술되는 저자의 관심분야를 소셜 네트워크 분석기법을 통해 다양한 측면에서 분석하고자 한다. 분석결과 많은 저자들이 임베디드, 보안, 영상처리, 무선통신, 빅데이터, USN, 네트워크, RFID 등의 관심분야를 중심으로 연구하고 있음을 알 수 있다.

• 한국IT서비스학회지
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• 제10권4호
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• pp.119-131
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• 2011

When your company buys packaged software, the company needs to determine how many software licenses must be purchased. If the quantity is too small, it will be not enough for the users to access the programs at anytime within the company. Conversely, if the quantity is too large, they will waste a lot of money. In this paper, we surveyed several number of researchers of K institute and derived the using pattern of a specific packaged software. We estimated a proper number of packaged software licenses by Erlang Loss Function, the Engset Model, and simulation model. With the results of the three methodologies, we were able to empirically verify economic benefits of packaged software purchase by comparing NPV (Net Present Value) between user licenses and network licenses. Consequently, TCO of user licenses is much higher than that of network licenses. We had probabilistically calculated proper number of licenses based on the using pattern of users. Hence, this paper will be useful for decision makers who are going to determine package software's type and quantity from an economic perspective.

• 한국측량학회지
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• 제26권6호
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• pp.549-559
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• 2008

본 연구에서는 다양한 조건 하에서의 Bernese와 TGO와의 처리결과 비교를 통하여 상용 소프트웨어의 한계와 정밀측위에 대한 응용성을 검증하였다. 이를 위하여 전국규모의 세 가지의 관측데이터와 그 보다 작은 두 가지의 지역 데이터를 선정하여 망을 구성하고 Bernese와 TGO를 사용하여 기선해석 및 망조정을 통해 성과를 산출하여 소프트웨어별, 기선거리 및 망규모별, 관측시간별, 고정점 수별로 비교분석을 실시하였다. 소프트웨어 간 비교에서는 학술연구용 소프트웨어의 정확도가 우수하였다. 비록 GPS 관련 기술이 발달하면서 수신기의 정확도가 향상되었고 이에 병행하여 상용 소프트웨어도 발전을 거듭해왔으나 학술연구용 소프트웨어와의 평균성과차이를 볼 때 크지는 않지만 엄연한 차이가 존재했다. 따라서 가장 정밀한 위치정보가 요구될 때는, 특히 기선벡터가 큰 경우에는 필히 학술연구용 소프트웨어를 사용하여야 할 것이다.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.189-202
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• 2023

In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• ETRI Journal
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• 제33권5호
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• pp.770-779
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• 2011

Security quad and cube (SQC) is a network attack analyzer that is capable of aggregating many different events into a single significant incident and visualizing these events in order to identify suspicious or illegitimate behavior. A network administrator recognizes network anomalies by analyzing the traffic data and alert messages generated in the security devices; however, it takes a lot of time to inspect and analyze them because the security devices generate an overwhelming amount of logs and security events. In this paper, we propose SQC, an efficient method for analyzing network security through visualization. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacks. In addition, by providing a detailed analysis of network attacks, this method can more precisely detect and distinguish them from normal events.

• 한국컴퓨터산업학회논문지
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• 제2권9호
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• pp.1191-1198
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• 2001

소프트웨어 개발에서 점점 더 중요시되는 사항은 개발 생명주기의 초기에 개발과 관련된 노력과 비용을 추정하는 능력이다. 제안된 모델 대부분은 경험 데이터의 직관, 전문가 판단과 회귀분석의 조합에 기반을 두고 있으나 다양한 환경에 적용될 수 있는 하나의 모델을 개발하는 것이 불가능하였다. 본 논문은 기능 구성요소 형태들로 측정된 소프트웨어 규모로 소프트웨어 개발노력을 추정하는 신경망 모델을 제안한다. 신경망의 은닉뉴런 수는 입-출력 관계로부터 휴리스틱하게 얻는 방법을 제안한다. 24개 소프트웨어 개발 프로젝트 사례연구를 통해 적합한 신경망 모델을 제시하였다. 또한, 회귀분석 모델과 신경망 모델을 비교하여 신경망 모델의 정확성이 보다 좋음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.484-510
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• 2017

Network security is rapidly developing, but so are attack methods. Network worms are one of the most widely used attack methods and have are able to propagate quickly. As an active defense approach to network worms, the honeynet technique has long been limited by the closed architecture of traditional network devices. In this paper, we propose a closed loop defense system of worms based on a Software-Defined Networking (SDN) technology, called Worm-Hunter. The flexibility of SDN in network building is introduced to structure the network infrastructures of Worm-Hunter. By using well-designed flow tables, Worm-Hunter is able to easily deploy different honeynet systems with different network structures and dynamically. When anomalous traffic is detected by the analyzer in Worm-Hunter, it can be redirected into the honeynet and then safely analyzed. Throughout the process, attackers will not be aware that they are caught, and all of the attack behavior is recorded in the system for further analysis. Finally, we verify the system via experiments. The experiments show that Worm-Hunter is able to build multiple honeynet systems on one physical platform. Meanwhile, all of the honeynet systems with the same topology operate without interference.

• 한국게임학회 논문지
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• 제11권6호
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• pp.53-60
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• 2011

게임 서비스를 위한 소프트웨어의 경우 그 신뢰도에 대한 중요성은 지속적으로 증가하고 있다. 따라서 소프트웨어 신뢰도에 대한 평가 또한 매우 중요하다. 본 연구에서는 개발 공정에 대한 대표 활동인 확인 및 검증이 소프트웨어 신뢰도에 어떠한 영향을 미치는가를 정량적으로 분석하기 위하여 실험을 수행하였다. 이러한 실험 결과는, BBN (Bayesian Belief Network) 기반 신뢰도 평가와 같이, 개발 공정에 근거하여 신뢰도를 평가할 때 매우 유용한 근거 자료로 활용될 것이다.