• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.85-101
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• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.81-98
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• 2009

Collaborative filtering, one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information and supporting the decision making. However, despite of its success and popularity, one notable issue is incredibility of recommendations by unreliable users called shilling attacks. To deal with this problem, in this paper, we analyze the type of shilling attacks and propose a unique method of building a model for protecting the recommender system against manipulated ratings. In addition, we present a method of applying the model to collaborative filtering which is highly robust and stable to shilling attacks.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.670-679
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• 2015

With a rapid development of internet, many users these days refer to various recommender sites when buying items, movies, music and more. However, there are malicious users (Sybil) who raise or lower item ratings intentionally in these recommender sites. And as a result, a recommender system (RS) may recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings generated by users into normal ratings and outlier ratings, and to minimize the effects of malicious users. Specifically, our algorithm first ensures a stable RS against three kinds of attack models (Random attack, Average attack, and Bandwagon attack) which are the main recent security issues in RS. To prove the performance of the method of suggestion, we conducted performance analysis on real world data that we crawled. The performance analysis demonstrated that the suggested method performs well regardless of Sybil size and type when compared to existing algorithms.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.119-140
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• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.953-960
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• 2016

Due to the prevalent use of online systems and the increasing amount of accessible information, the influence of recommender systems is growing bigger than ever. However, there are several attempts by malicious users who try to compromise or manipulate the reliability of recommender systems with cyber-attacks. By analyzing the ratio of 'sympathy' against 'apathy' responses about a concerned review and reflecting the results in a recommendation system, we could present a way to improve the performance of a recommender system and maintain a robust system. After collecting and applying actual movie review data, we found that our proposed recommender system showed an improved performance compared to the existing recommendation systems.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.129-142
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• 2019

The interest in the blockchain technology has been increasing since its inception and it has been applied to many fields and sectors. The blockchain technology creates a decentralized environment where no third party controls the data and transaction. Mobile apps recommendation has been extensively used to recommend apps to mobile users. For example, Android-based recommendation applications have been developed to recommend other mobile apps for download depending on user's preferences and mobile context. These recommendations help users discover apps by referring to the experiences of other users. Due to the collection of a large amount of data and user information, there is a problem of insecurity and user's privacy that are prone to be attacked. To address this issue the blockchain technology can be incorporated to assure cryptographic safety. In this paper, we present a survey of the on-going mobile app recommendations and e-commerce technology trend to address how the blockchain can be incorporated into the collaborative filtering recommendation systems to enable the users to set up a secured data, which implies the importance of user privacy preference on personalized app recommendations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.81-92
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• 2023

A privacy model is a technique that quantitatively restricts the possibility and degree of privacy breaches through privacy attacks. Representative models include k-anonymity, l-diversity, t-closeness, and differential privacy. While many privacy models have been studied, research on selecting the most suitable model for a given dataset has been relatively limited. In this study, we develop a system for recommending the suitable privacy model to prevent privacy breaches. To achieve this, we analyze the data features that need to be considered when selecting a model, such as data type, distribution, frequency, and range. Based on privacy model background knowledge that includes information about the relationships between data features and models, we recommend the most appropriate model. Finally, we validate the feasibility and usefulness by implementing a recommendation prototype system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.11
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• pp.1050-1067
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• 2014

CAS(Close Air Support) is aircraft attack against hostile targets that are in close proximity to friendly forces. Immediate CAS is the mission that attack unplanned targets, and especially the distribution of suitable aircraft assets makes huge effect on the result of immediate CAS mission. But It is hard to find a previous studies on immediate CAS sortie distribution with aircraft suitability. This study suggests a methodology with aircraft suitability for immediate CAS sortie distribution. The methodology consists of 3 steps. Firstly, we analyze target information for situational awareness. Secondly, we calculate each aircraft's suitability value per each target based on the result of previous analysis. Lastly, we suggest immediate CAS sortie distribution based on the aircraft adoptability value to a decision maker. This methodology will provide not only quantitative analysis, but also decision making of immediate CAS sortie distribution more timely and effectively.

• Journal of Platform Technology
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• v.9 no.3
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• pp.3-17
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• 2021

Advanced persistent threat (APT) attacks are attacks aimed at a particular entity as a set of latent and persistent computer hacking processes. These APT attacks are usually carried out through various methods, including spam mail and disguised banner advertising. The same name is also used for files, since most of them are distributed via spam mail disguised as invoices, shipment documents, and purchase orders. In addition, such Infostealer attacks were the most frequently discovered malicious code in the first week of February 2021. CDR is a 'Content Disarm & Reconstruction' technology that can prevent the risk of malware infection by removing potential security threats from files and recombining them into safe files. Gartner, a global IT advisory organization, recommends CDR as a solution to attacks in the form of attachments. There is a program using CDR techniques released as open source is called 'Dangerzone'. The program supports the extension of most document files, but does not support the extension of HWP files that are widely used in Korea. In addition, Gmail blocks malicious URLs first, but it does not block malicious URLs in mail systems such as Naver and Daum, so malicious URLs can be easily distributed. Based on this problem, we developed a 'Dangerzone' program that supports the HWP extension to prevent APT attacks, and a Chrome extension that performs URL checking in Naver and Daum mail and blocking banner ads.