결정트리 기반의 기계학습을 이용한 동적 데이터에 대한 재익명화기법

김영기(Young Ki Kim),홍충선(Choong Seon Hong) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.1

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

Identifying Unusual Days

Kim, Min-Kyong,Kotz, David Korean Institute of Information Scientists and Eng 2011 Journal of Computing Science and Engineering Vol.5 No.1

Pervasive applications such as digital memories or patient monitors collect a vast amount of data. One key challenge in these systems is how to extract interesting or unusual information. Because users cannot anticipate their future interests in the data when the data is stored, it is hard to provide appropriate indexes. As location-tracking technologies, such as global positioning system, have become ubiquitous, digital cameras or other pervasive systems record location information along with the data. In this paper, we present an automatic approach to identify unusual data using location information. Given the location information, our system identifies unusual days, that is, days with unusual mobility patterns. We evaluated our detection system using a real wireless trace, collected at wireless access points, and demonstrated its capabilities. Using our system, we were able to identify days when mobility patterns changed and differentiate days when a user followed a regular pattern from the rest. We also discovered general mobility characteristics. For example, most users had one or more repeating mobility patterns, and repeating mobility patterns did not depend on certain days of the week, except that weekends were different from weekdays.

Automatic Product Review Helpfulness Estimation based on Review Information Types

Munhyong Kim(김문형),Hyopil Shin(신효필) Korean Institute of Information Scientists and Eng 2016 정보과학회논문지 Vol.43 No.9

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.

기계학습을 이용한 네트워크 전장정보 수집

한규석(Kyu Seok Han),김태규(Taekyu Kim),심신우(Shinwoo Shim),전성구(Sung Goo Jun),윤지원(Jiwon Yoon) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.10

Recently, various systems based on Internet of Things (IOT) and Information and Communications Technologies(ICT) have been developed. Today, assorted devices are connected to a network, and various operating systems according to devices having different resources and functions have appeared. With the increased need for in hacking security, researches on the vulnerability analysis of the operating system installed on each device and the actual attack technique have been carried out. Accordingly, the type and detailed version of the operating system of the device, Function (API) is emerging as important information in security. Since the control of this information gathering in the cyber warfare is the first stage of the cyber threat, many studies have been conducted on mehods for controlling the network traffic while scanning. In order to bypass this control of the network, information collectors prepare countermeasures to secretly collect port information. In this paper, we deal with a scanning method that can acquire information about opponents through network basic commands which are not important in the network control system.

Stacked Bidirectional LSTM-CRFs를 이용한 한국어 의미역 결정

배장성(Jangseong Bae),이창기(Changki Lee) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.1

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

심층학습 기반의 Predictor-Estimator 모델을 이용한 영어-한국어 기계번역 품질 예측

김현(Hyun Kim),신재훈(Jaehun Shin),이원기(Wonkee Lee),조승우(Seungwoo Cho),이종혁(Jong-Hyeok Lee) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.6

Quality Estimation (QE) for machine translation is an automatic method for estimating the quality of machine translation output without the need to use reference translations. QE has recently grown in importance in the field of machine translation (MT). Recent studies on QE have mainly focused on European languages, whereas fewer studies have been carried out on QE for Korean. In this paper, we create a new QE dataset for English to Korean translations and apply a neural network based Predictor-Estimator model to a QE task of English-Korean. Creating a QE dataset requires manual post-edited translations for MT outputs. Because Korean is a free word order language and allows various writing styles for translation, we provide guidance for creating manual post-edited Korean translations for English-Korean QE data. Also, we alleviate the imbalanced data problem of QE data. Finally, this paper reports on our experimental results of the QE task of English-Korean by using the Predictor-Estimator model trained from the created English-Korean QE data.

C 시큐어 코딩 규칙 분류를 통한 실행 안전성을 보장하는 한글 언어 설계

김연어(Yeoneo Kim),송지원(Jiwon Song),우균(Gyun Woo) Korean Institute of Information Scientists and Eng 2015 정보과학회논문지 Vol.42 No.4

Since most of information is computerized nowadays, it is extremely important to promote the security of the computerized information. However, the software itself can threaten the safety of information through many abusive methods enabled by coding mistakes. Even though the Secure Coding Guide has been proposed to promote the safety of information by fundamentally blocking the hacking methods, it is still hard to apply the techniques on other programming languages because the proposed coding guide is mainly written for C and Java programmers. In this paper, we reclassified the coding rules of the Secure Coding Guide to extend its applicability to programming languages in general. The specific coding guide adopted in this paper is the C Secure Coding Guide, announced by the Ministry of Government Administration and Home Affairs of Korea. According to the classification, we applied the rules of programming in Sprout, which is a newly proposed Korean programming language. The number of vulnerability rules that should be checked was decreased in Sprout by 52% compared to C.

Protecting the iTrust Information Retrieval Network against Malicious Attacks

Chuang, Yung-Ting,Melliar-Smith, P. Michael,Moser, Louise E.,Lombera, Isai Michel Korean Institute of Information Scientists and Eng 2012 Journal of Computing Science and Engineering Vol.6 No.3

This paper presents novel statistical algorithms for protecting the iTrust information retrieval network against malicious attacks. In iTrust, metadata describing documents, and requests containing keywords, are randomly distributed to multiple participating nodes. The nodes that receive the requests try to match the keywords in the requests with the metadata they hold. If a node finds a match, the matching node returns the URL of the associated information to the requesting node. The requesting node then uses the URL to retrieve the information from the source node. The novel detection algorithm determines empirically the probabilities of the specific number of matches based on the number of responses that the requesting node receives. It also calculates the analytical probabilities of the specific numbers of matches. It compares the observed and the analytical probabilities to estimate the proportion of subverted or non-operational nodes in the iTrust network using a window-based method and the chi-squared statistic. If the detection algorithm determines that some of the nodes in the iTrust network are subverted or non-operational, then the novel defensive adaptation algorithm increases the number of nodes to which the requests are distributed to maintain the same probability of a match when some of the nodes are subverted or non-operational as compared to when all of the nodes are operational. Experimental results substantiate the effectiveness of the detection and defensive adaptation algorithms for protecting the iTrust information retrieval network against malicious attacks.

Designing Technology for Visualisation of Interactions on Mobile Devices

Deray, Kristine,Simoff, Simeon Korean Institute of Information Scientists and Eng 2009 Journal of Computing Science and Engineering Vol.3 No.4

Interactions are intrinsic part of what we do. We interact when we work, when we learn, when we visit a doctor, and when we play. With the advent of information and communications technology we can collect rich data (video, audio, and various transcripts including text chat) about such interactions. This opens an opportunity to monitor the dynamics of interactions and to get deeper insights of how they unfold and deliver this information to the interacting parties. This paper presents the design of a technology for visualising information about the dynamics of unfolding of interactions and presenting it in an ambient display on mobile devices. The purpose of this technology is the delivery of such information to the point of decision making.

콜드 스타트 문제 완화를 위한 가중치 기반 다중 도메인 추천 시스템

문선아(Seona Moon),고상기(Sang-Ki Ko) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.10

A recommendation system predicts users’ ratings based on users’ past behaviors and item preferences. One of the most famous types of recommendation systems is the collaborative filtering method that predicts users’ ratings based on the rating information from users with similar preferences. In order to predict the preferences of users, we need adequate information about users’ interactive information on items. Otherwise, it is very difficult to make accurate predictions for users without adequate information. This phenomenon is called the cold-start problem. In this paper, we propose a multi-domain recommendation system that utilizes the rating information of multiple domains. We propose a method that calculates the weight of each auxiliary domain to maximize the confidence of predicted ratings from multiple auxiliary domains and verify the performance of the proposed method through extensive experiments. As a result, we demonstrate that our algorithm produces better recommendation results compared to the classical algorithms simply utilized in multiple domain settings.