• International Journal of Contents
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• 제10권3호
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• pp.1-8
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• 2014

In this paper, we collect and analyze the status of mobile phone applications (hereafter apps) in the healthcare and fitness category of the Apple iTunes App Store and Google Play Store. We determine the number of apps and analyze statistical aspects such as classifications, age rating, fees, and user evaluation of the popular items. As of September 30, 2013, there were 236 popular apps available from iTunes. Google Play offered 720 apps. We discover that apps for healthcare and fitness are diverse. Apps for physical exercise have the greatest popularity. The proportions of apps that are suitable for all ages among the Google and iTunes popular apps are 55.8% and 89.4%, respectively. The user evaluation of apps in iTunes is relatively less positive. We determine that the proportion of paid apps to free apps in Google is higher than that of the apps in iTunes. We perform hypothesis tests and find statistically significant differences in age rating and perceived satisfaction between the apps of the Apple iTunes App Store and Google Play Store. However, we find no meaningful differences in the classification and price of the apps between the two app stores. We perform hypothesis tests to verify the differences in age rating and perceived satisfaction between the paid and free apps within and across the Google Play Store and iTunes App Store. There are statistically significant differences in the age rating between the paid and free apps in the Google play store, between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps. There are statistically significant differences in the perceived satisfaction between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• 한국정보통신학회논문지
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• 제26권6호
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• pp.907-913
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• 2022

전 세계 스마트폰 이용자 중 약 70%가 안드로이드 운영체제 기반 스마트폰을 사용하고 있으며 이러한 안드로이드 플랫폼을 표적으로 한 악성 앱이 지속적으로 증가하고 있다. 구글은 증가하는 안드로이드 대상 악성코드에 대응하기 위해 'Google Play Protect'를 제공하여 악성 앱이 스마트폰에 설치되는 것을 방지하고 있으나, 아직도 많은 악성 앱들이 정상 앱처럼 위장하여 구글 플레이스토어에 등록되어 선량한 일반 사용자의 스마트폰을 위협하고 있다. 하지만 일반 사용자가 악성 앱을 점검하기에는 상당한 전문성이 필요하기에 대부분 사용자는 안티바이러스 프로그램에 의존하여 악성 앱을 탐지하고 있다. 이에 본 논문에서는 앱에서 쉽게 확인이 가능한 카테고리와 권한만을 활용하여 앱의 불필요한 악성 권한을 분류하고 분류한 권한을 통해 악성 앱을 쉽게 검출할 수 있는 방법을 제안한다. 제안된 방법은 '상용 악성 앱 검출 프로그램'과 미탐율·오탐율 측면에서 비교 분석하여 성능 수준을 제시하고 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.201-203
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• 2019

Google Play에 새로운 콘텐츠들이 나오고 경쟁함으로써 앱과 게임의 크기는 지속적으로 증가하고 있다. 앱과 게임의 크기가 커질수록 Google Play 스토어를 통한 앱 설치가 줄어들고 있다. 본문은 기존 지원 모델인 APK에 대한 구조 및 한계에 대해 이야기하고 새로운 지원 모델인 AAB(Android App Bundle) 구조에 대해 이야기한다. 추가로 향후 전망을 해보고자 한다.

• ETRI Journal
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• 제43권1호
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• International Journal of Internet, Broadcasting and Communication
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• 제8권4호
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• pp.11-18
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• 2016

Due to open source policy, Android systems are exposed to a variety of security problems. In particular, app reuse attacks are detrimental threat to the Android system security. This is because attacker can create core malign components and quickly generate a bunch of malicious apps by reusing these components. Hence, it is very imperative to discern whether Android apps contain reused components. To meet this need, we propose an Android app reuse analysis technique based on the Sequential Hypothesis Testing. This technique quickly makes a decision with a few number of samples whether a set of Android apps is made through app reuse. We performed experimental study with 6 malicious app groups, 1 google and 1 third-party app group such that each group consists of 100 Android apps. Experimental results demonstrate that our proposed analysis technique efficiently judges Android app groups with reused components.

• 한국정보시스템학회지:정보시스템연구
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• 제27권2호
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• pp.1-28
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• 2018

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.

• 정보보호학회논문지
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• 제27권6호
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• pp.1491-1498
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• 2017

스마트폰의 사용 및 다양한 앱들의 출시 등이 급격하게 증가됨에 따라 악성 앱들도 많이 증가하였고 이로 인한 피해가 속출하고 있다. 안드로이드 앱들이 등록되는 구글 마켓은 앱 등록 규정이 있음에도 불구하고 정상적인 앱들과 악성 앱들이 불가피하게 동시에 존재한다. 특히, 소셜 네트워크가 활성화됨에 따라 안드로이드 구글 마켓에서도 다양한 형태로 보이지 않게 사용자들이 소셜 정보망을 맺고 평점, 다운로드 수 및 인지도 정보 등이 참고 되어 앱 다운로드 수에 반영되고 있다. 결과, 일반 사용자들이 단순히 평점, 인기도, 인기 있는 댓글 및 인지도 높은 카테고리 앱 등만 반영하여 앱을 선택하게 되면 악성 앱 다운로드로 인해 때로는 큰 피해를 볼 수 있다. 따라서 본 연구는 실제 운용되고 있는 안드로이드 마켓에서 장기간 소셜 정보를 직접 크롤링하고 분석하여 악성 앱의 경향성을 처음으로 분석했다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권1호
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• pp.17-24
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• 2023

모바일 앱은 사용자의 편의를 위해 개인정보에 접근할 수 있는 권한을 자주 요청한다. 하지만 이에 따라 모바일 앱을 이용하는 동안 허용되지 않은 개인정보가 유출되는 문제가 많이 발생했다. 이러한 문제를 해결하기 위해 구글 앱스토어에 등록된 앱은 개인정보 처리방침에 사용자의 개인정보를 앱에서 어떻게 활용하는지 명시하도록 했다. 하지만 앱이 수행하는 개인정보 수집 및 처리 과정이 개인정보 처리방침에 정확히 공개되어 있는지 확인하기 어려우며, 모바일 앱 사용자가 앱이 접근할 수 있는 개인정보에 대해 알기 위해서는 개인정보 처리방침에 의존해야만 한다. 본 연구에서는 개인정보 처리방침과 모바일 앱을 분석하여 개인정보 처리방침의 신뢰성을 확인하는 시스템을 제시한다. 먼저 개인정보 처리방침의 텍스트를 추출 및 분석하여 모바일 앱이 어떤 개인정보를 이용할 수 있다고 공개하는지 확인한다. 이후 안드로이드 정적 분석을 통해 앱이 접근할 수 있는 개인정보 분류를 확인하고, 두 결과를 비교하여 개인정보 처리방침을 신뢰할 수 있는지 분석한다. 실험을 위해 구글 앱스토어에 등록된 약 13,000개 안드로이드 앱의 패키지 파일과 부가정보를 수집한 뒤 분석할 수 있는 앱을 선정하기 위해 4가지 조건에 따라 전처리를 진행했다. 선정한 앱을 대상으로 텍스트 분석과 모바일 앱 분석을 진행하고, 이를 비교하여 모바일 앱은 개인정보 처리방침에 공개한 것보다 더욱 많은 개인정보에 접근할 수 있음을 증명한다.

• 한국IT서비스학회지
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• 제14권2호
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• pp.253-273
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• 2015

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Categorized as one kind of cloud computing, SaaS is computing resource and software sharing model with can be accessed via the internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level and performance in company. Therefore this research limited cloud services to SaaS especially focused on collaborative application service, and attempts to identify the factors which impact the performance of collaboration and intention to use. This study adopts technological factors of cloud SaaS services and factors of task characteristics to explore the determinants of collaborative performance and intention to use. An experimental study using student subjects with Google Apps provided empirical validation for our proposed model. Based on 337 data collected from respondents, the major findings are following. First, the characteristics of cloud computing services such as collaboration support, service reliability, and ease of use have positive effects on perceived usefulness of collaborative application while accessability, service reliability, and ease to use have positive effects on intention to use. Second, task interdependence has a positive effects on collaborative performance while task ambiguity factor has not. Third, perceived usefulness of collaborative application have positive effects on intention to use.