• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.631-646
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• 2020

The government is pursuing a policy to remove plug-ins for public and private websites to create a convenient Internet environment for users. In general, financial institution websites that provide financial services, such as banks and credit card companies, operate fraud detection system(FDS) to enhance the stability of electronic financial transactions. At this time, the installation software is used to collect and analyze the user's information. Therefore, there is a need for an alternative technology and policy that can collect user's information without installing software according to the no-plug-in policy. This paper introduces the device fingerprinting that can be used in the standard web environment and suggests a guideline to select from various techniques. We also propose a user authentication model using device fingerprints based on machine learning. In addition, we actually collected device fingerprints from Chrome and Explorer users to create a machine learning algorithm based Multi-class authentication model. As a result, the Chrome-based Authentication model showed about 85%~89% perfotmance, the Explorer-based Authentication model showed about 93%~97% performance.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.389-397
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• 2017

Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.33-38
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• 2019

In this paper, we propose a new blockchain technology that could comply with GDPR. The proposed model can prevent illegal access by controlling access to the personal information according to a access policy. For example, it can control access to the information on a role-basis and information validation period. The core mechanism of the proposed model is to encrypt the personal information with public key which is associated with users attributes policy, and then decrypt it with a private key and users attributes based on a Attribute-based Encryption scheme. It can reduce a trusted third-part risk by replacing it with a number of nodes selected from the blockchain. And also the private key is generated in the form of one-time token to improve key management efficiency. We proved the feasibility by simulating the proposed model using the chaincode of the Hyperledger Fabric and evaluate the security.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.7
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• pp.929-937
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• 2016

The purpose of this study was to investigate the effect of Acanthopanax senticosus extract (ASE) (ethanol : DW=1:1, v/v) on inhibition of type 2 diabetes using an OLETF rat model via regulation of HbA1c and AGEs levels. Supplementation with ASE 0.1% and 0.5% effectively lowered levels of glucose, insulin, oral glucose tolerance test, and Homa-insulin resistance, suggesting reduced insulin resistance. Blood levels of HbA1c and AGEs were significantly reduced in a dose-dependent manner. As oxidative stress plays a key role in accelerating production of HbA1c and AGEs, which worsen symptoms of type 2 diabetes, levels of malonaldehyde and pro-inflammatory cytokines were measured. Lipid peroxidation in both blood and liver tissues was significantly reduced, and induction of pro-inflammatory cytokines interleukin-${\beta}$ and tumor necrosis factor-${\alpha}$, which elevate production of HbA1c and AGEs, was inhibited (P<0.05). To evaluate the possible cellular events after AGEs receptor activation, genetic expression of protein kinase C (PKC)-${\delta}$ and transforming growth factor (TGF)-${\beta}$ was measured by real-time polymerase chain reaction. Supplementation with both ASE 0.1% and 0.5% significantly inhibited mRNA expression of PKC-${\delta}$ and TGF-${\beta}$, indicating that ASE may have beneficial effects on preventing insulin-resistant cells or tissues from progressing to diabetic complications. Taken together, ASE has potential to improve type 2 diabetes by inhibiting insulin resistance and protein glycosylation, including production of HbA1c and AGEs. Anti-oxidative activities of ASE are a main requisite for reducing production of HbA1c and AGEs and are also related to regulation of the PKC signaling pathway, resulting in suppression of TGF-${\beta}$, which increases synthesis of collagen, prostaglandin, and disease-related proteins.

• Journal of KIISE
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• v.42 no.3
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• pp.320-327
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• 2015

Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.

• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.529-536
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• 2024

Recent advancements in AI technology have led to its application across various fields. However, the lack of transparency in AI operations makes it challenging to guarantee the quality of its outputs. Therefore, we integrate requirements engineering in software engineering with conversational AI technology to ensure procedural fairness. Traditional requirements engineering research uses grammar-centered analysis, which often fails to fully interpret the semantic aspects of natural language. To solve this, we suggest combining Noam Chomsky's syntactic structure analysis with Charles Fillmore's semantic role theory. Additionally, we extend our previous research by analyzing adjectives in informal requirement sentence structures. This enables precise emotional analysis of the main characters in comics. Based on the results of the analysis, we apply the emotional states of the objects to the states in the UML state diagram. Then, we create the 3D object with Three.js based on the object that reflects the emotional states in the state diagram. With this approach, we expect to represent the emotional state of a 3D object.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.124-124
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• 2016

하천에서의 분변성대장균은 분변성 오염 정도를 나타내는 지표로서, 이 농도가 높을수록 오염된 하천수와의 접촉을 통한 호흡기, 소화기 및 피부 관련 질병의 발발 확률이 높다고 알려져 있다. 따라서 하천에서의 수영, 수상스키 등과 같은 입수형 친수활동을 할 때, 분변성대장균 농도가 농도 기준 이하인지를 확인하고 이러한 정보를 친수활동에 이용할 필요가 있다. 그러나 분변성대장균의 경우, 현재 자동수질측정망에서 측정되고 있는 다른 수질인자들과는 달리 실시간 측정이 불가능하다고 알려져 있다. 분변성대장균을 측정하는데 있어 최소 18시간 이상이 필요하며, 이러한 분변성대장균 측정 방식은 하천 이용자들이 안전한 친수활동을 영위하는데 있어 적절한 수질 정보를 제공하지 못한다. 그러므로 분변성대장균을 예측하는 모델을 개발하고, 이를 이용하여 실시간 분변성대장균 정보를 생성하여 하천 이용자들에게 제공할 필요가 있다. 본 연구에서는 친수활동이 활발하게 이루어지는 곳 중 하나인 북한강의 대성리 지점에 대해 데이터 기반 모델을 이용하여 분변성대장균을 예측하였다. 데이터 기반 모델은 물리 기반 모델에서 필요한 지형데이터나 비점오염원 등의 초기 오염물의 양에 대한 데이터를 필요로 하지 않고, 대신 독립변수로 사용되는 기상 및 수질데이터를 필요로 한다. 이러한 기상 및 수질데이터는 기존 기상관측소, 수질관측소에서 매일 자동으로 측정되기 때문에 데이터 기반 모델은 물리 기반 모델에 비해 입력데이터를 구성하기가 쉽다는 장점을 지닌다. 이러한 데이터 기반 모델 중 분류 모델은 회귀 모델과 달리 분변성대장균 농도가 일정 수질기준 이상을 넘는지를 바로 예측할 수 있다. 본 연구에서는 분류 모델 중 높은 예측력을 가진다고 알려진 랜덤포레스트(random forest) 기법을 이용하여 분변성대장균 예측 모델을 개발하였다. 분변성대장균 예측 모델은 주어진 기상 및 수질 조건에 대해 분변성대장균이 200 CFU/100ml가 넘는지를 예측하였다. 예측된 분변성대장균이 기준을 넘는 경우를 2등급, 넘지 않는 경우를 1등급으로 명명하였다. 모델을 개발하기 위하여 북한강 대성리 인근 측정소에서 2010년부터 2015년까지 측정된 기상 및 수질데이터를 수집하였다. 수집한 데이터를 훈련 및 검증데이터로 샘플링하였으며, 이 때 샘플링한 데이터가 기존 데이터가 가지고 있던 등급별 비율을 유지하기 위하여 층화샘플링을 하였다. 본 연구에서는 샘플링에 의한 불확실성을 줄이기 위하여 랜덤하게 50번 샘플링된 각각의 훈련데이터에 대해 모델을 개발하였다. 50개의 모델의 검증 결과를 종합한 결과, 전체 예측률은 0.139로 나타났다.