• Journal of Information Technology Services
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• v.17 no.2
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• pp.111-128
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• 2018

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.

• The Journal of Information Systems
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• v.27 no.2
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.77-86
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• 2014

Mobile devices are generally using app images which are artificially designed. Accordingly, this paper presents adjusting device brightness based on app image categorization for enhancing the visibility under various light condition. First, the proposed method performed two prior subjective tests under various lighting conditions for selecting features of app images concerning visibility and for selecting satisfactory range of device brightness for each app image. Then, the relationship between selected features of app image and satisfactory range of device brightness is analyzed. Next, app images are categorized by using two features of average brightness of app image and distribution ratio of advanced colors that are related to satisfaction range of device brightness. Then, optimal device brightness for each category is selected by having the maximum frequency of satisfaction device brightness. Experimental results show that the categorized app images with optimal device brightness have high satisfaction ratio under various light conditions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.657-667
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• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.77-86
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• 2017

This study selected car-related mobile apps for app developers suffering from low revenue and classified car apps assisting users in driving or managing a car. A total of 697 car apps were classified into eight categories. Most apps are in four categories: car news & information (28%), locating service (23%), car rental service (15%), safe/efficient driving service (12%). The remaining categories are buying & selling, driver's communication, maintenance management, and expenses monitoring. Many apps are simple and too similar in their main functions. Only a few apps are designed to be more comprehensive and have functions in two or more categories. For the practicality of the categorization scheme, this study checked the inter-rater reliability in two tests and got 0.886 and 0.828. The result from this study suggests functions that are not implemented yet or need to be combined. Future research will focus on identifying promising car apps or designing multi-functional car apps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.1-7
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• 2020

We can build regular customer relationships combining SNS (social networking service) with shopping mall like offline trade. A customer who once purchased is registered as reaular and the relationship continues afterward. The registered regular customer get sthe information about objective product shipment and besides it, he contacts with a story of frams, growth of vegetables, sows to harvests. Consumer can purchase with one click necessary foods as he looks at timeline. Sellers give information about news. discounts to customers. Besides it, food storages, recipes can be given to consumers. The good point here is that selling and promoting can be performed within one account. This is better than link is provided for selling an promoting separately. Like this, besides personal connections using SNS, categorization function gives consumers on line shopping mall service. Once the consumer purchase, he is registered as regular. Besides, the consumers who do not know each other, can share information, suggest products, spread the news.