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題名 無人機操控應用程式之真實性檢視
Examining the Authenticity of Drone Control Applications
作者 張維暘
Chang, Wei-Yang
貢獻者 杜雨儒<br>蔡瑞煌
Tu, Yu-Ju<br>Tsaih, Rua-Huan
張維暘
Chang, Wei-Yang
關鍵詞 無人機操作應用程式
官方來源辨識
自然語言處理
使用者評論分析
深度學習
資訊安全
訊號理論
使用者信任
Drone Control applications
Official Source Identification
Natural Language Processing (NLP)
User Review Analysis
Deep Learning
Information Security
Signaling Theory
User Trust
日期 2025
上傳時間 4-Aug-2025 14:26:47 (UTC+8)
摘要 隨著無人機技術的迅速普及，各類控制應用程式在行動平台上大量湧現，使使用者能夠便利地透過手機操作無人機。然而，在應用商店中，部分標榜特定品牌名稱的應用實際上並非由原廠官方上架，可能造成使用者在不知情的情況下下載未經授權之應用程式，進而引發資訊安全與操作風險。 為因應此問題，本研究建立一套自動辨識架構，分析使用者對無人機應用程式的評論內容，以判斷該應用是否具有官方來源特徵。研究資料來自公開平台上的用戶評論與相關資訊頁面，並透過語言特徵建構分類模型，辨識應用程式在「官方／非官方」與「真實／假冒」兩種維度上的可能性。此外，本研究亦設計問卷實驗，驗證該分類結果是否能顯著影響使用者對應用程式的信任、下載意願與付費傾向。
With the rapid proliferation of drone technology, a wide variety of control applications has emerged on mobile platforms, enabling users to operate drones conveniently via smartphones. However, some applications listed on app marketplaces, despite bearing brand-like names, are not officially released by manufacturers. This can result in users unknowingly downloading unauthorized apps, potentially posing information security and operational risks. To address this issue, this study proposes an automated identification framework that analyzes user reviews of drone control apps to assess whether the apps exhibit signs of official origin. The dataset is composed of user-generated content and metadata retrieved from public platforms such as Google Play. Based on linguistic features, classification models were trained to estimate the likelihood of an app being “official/unofficial” and “real/fake” across two dimensions. Additionally, a user experiment was conducted to evaluate whether these classification outputs significantly influence users’ trust, download intentions, and willingness to pay.
參考文獻 [1]. Asatiani, A., Malo, P., Nagbøl, P., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021). Sociotechnical envelopment of artificial intelligence: an approach to organizational deployment of inscrutable artificial intelligence systems. Journal of the Association for Information Systems, 22(2), 325-352. [2]. Boyd, D., Kannan, P., & Slotegraaf, R. (2019). Branded apps and their impact on firm value: a design perspective. Journal of Marketing Research, 56(1), 76-88. [3]. Bygstad, B. and Øvrelid, E. (2020). Architectural alignment of process innovation and digital infrastructure in a high-tech hospital. European Journal of Information Systems, 29(3), 220-237. [4]. Chen, H., He, D., Zhu, S., & Yang, J. (2017). Toward detecting collusive ranking manipulation attackers in mobile app markets., 58-70. [5]. Chiu, C. and Huang, H. (2015). Examining the antecedents of user gratification and its effects on individuals’ social network services usage: the moderating role of habit. European Journal of Information Systems, 24(4), 411-430. [6]. Cram, W., Brohman, M., & Gallupe, R. (2015). Hitting a moving target: a process model of information systems control change. Information Systems Journal, 26(3), 195-226. [7]. Cram, W., Brohman, M., & Gallupe, R. (2016). Information systems control: a review and framework for emerging information systems processes. Journal of the Association for Information Systems, 17(4), 216-266. [8]. Davison, R. (2016). Transition arrangements to a new editorial structure. Information Systems Journal, 27(1), 1-3. [9]. Fu, S., Cai, Z., Lim, E., Liu, Y., Tan, C., Lin, Y., … & Deng, S. (2023). Unraveling the effects of mobile application usage on users’ health status: insights from conservation of resources theory. Journal of the Association for Information Systems, 24(2), 452-489. [10]. Ghazawneh, A. and Henfridsson, O. (2012). Balancing platform control and external contribution in third‐party development: the boundary resources model. Information Systems Journal, 23(2), 173-192. [11]. Karunanayake, N., Rajasegaran, J., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2020). A multi-modal neural embeddings approach for detecting mobile counterfeit apps: a case study on google play store. Ieee Transactions on Mobile Computing, 1-1. [12]. Karwatzki, S., Trenz, M., & Veit, D. (2022). The multidimensional nature of privacy risks: conceptualisation, measurement and implications for digital services. Information Systems Journal, 32(6), 1126-1157. [13]. Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus. Information Systems Journal, 25(6), 607-635. [14]. Keith, M., Babb, J., Lowry, P., Furner, C., & Abdullat, A. (2015). The role of mobile‐computing self‐efficacy in consumer information disclosure. Information Systems Journal, 25(6), 637-667. [15]. Kisembo, I., Ocen, G., Bongomin, O., Alunyu, A., Nibikora, I., Matovu, D., … & Bwire, F. (2021). An algorithm for improvement of email security on android operating system in the era of industry 4.0... [16]. Lehrer, C., Wieneke, A., Brocke, J., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems, 35(2), 424-460. [17]. Lin, Y., Chen, H., Brown, R., Li, S., & Yang, H. (2017). Healthcare predictive analytics for risk profiling in chronic care: a bayesian multitask learning approach. Mis Quarterly, 41(2), 473-495. [18]. Mettler, T. and Wulf, J. (2018). Physiolytics at the workplace: affordances and constraints of wearables use from an employee's perspective. Information Systems Journal, 29(1), 245-273. https://doi.org/10.1111/isj.12205 [19]. Miah, S., Gammack, J., & Hasan, N. (2017). Extending the framework for mobile health information systems research: a content analysis. Information Systems, 69, 1-24. [20]. Mishra, S. and Rana, G. (2019). Economics of counterfeit products: with special reference to mobile phones & watches. Theoretical Economics Letters, 09(05), 1699-1716. [21]. Murungi, D., Wiener, M., & Marabelli, M. (2019). Control and emotions: understanding the dynamics of controllee behaviours in a health care information systems project. Information Systems Journal, 29(5), 1058-1082. [22]. Ofe, H. and Sandberg, J. (2022). The emergence of digital ecosystem governance: an investigation of responses to disrupted resource control in the swedish public transport sector. Information Systems Journal, 33(2), 350-384. [23]. Porter, A. and Hooff, B. (2020). The complementarity of autonomy and control in mobile work. European Journal of Information Systems, 29(2), 172-189. [24]. Pujol, J., Arias, M., & Arratia, A. (2016). Geosrs: a hybrid social recommender system for geolocated data. Information Systems, 57, 111-128. [25]. Rajasegaran, J., Karunanayake, N., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2019). A multi-modal neural embeddings approach for detecting mobile counterfeit apps.. [26]. Rowe, F., Ngwenyama, O., & Richet, J. (2020). Contact-tracing apps and alienation in the age of covid-19. European Journal of Information Systems, 29(5), 545-562. [27]. Salo, M. and Frank, L. (2015). User behaviours after critical mobile application incidents: the relationship with situational context. Information Systems Journal, 27(1), 5-30. [28]. Sanyal, P., Menon, N., & Siponen, M. (2021). An Empirical Examination of the Economics of Mobile Application Security. MIS Quarterly, 45(4). [29]. Seneviratne, S., Kolamunna, H., & Seneviratne, A. (2015). A measurement study of tracking in paid mobile applications.. [30]. Shuraida, S., Gao, Q., Safadi, H., & Jain, R. (2024). The impact of feature exploitation and exploration on mobile application evolution and success. Journal of the Association for Information Systems, 25(3), 648-686. [31]. Spears, J. L., & Barki, H. (2010). User participation in information systems security risk management. Mis Quarterly, 34(3), 503. [32]. Sutanto, J., Palme, E., Tan, C., & Phang, C. (2013). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. Mis Quarterly, 37(4), 1141-1164. [33]. Tam, K., Feng, Y., & Kwan, S. (2019). The role of morality in digital piracy: understanding the deterrent and motivational effects of moral reasoning in different piracy contexts. Journal of the Association for Information Systems, 604-628. [34]. Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde, Jürgen Schupp, Gert G. Wagner (2011), Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences, Journal of the European Economic Association, Volume 9, Issue 3, 1 [35]. Tu, Y. J., & Piramuthu, S. (2024). Ethical consumerism, supply chains, and deceptions with RFID-based systems. Information & Management, 61(6), 104016. [36]. Tu, Y. J., & Piramuthu, S. (2024). Security and privacy risks in drone-based last mile delivery. European Journal of Information Systems, 33(5), 617-630. [37]. Tu, Y. J., & Shaw, M. J. (2009, December). An integrated approach to managing IT portfolio. In Workshop on E-Business (pp. 243-253). Berlin, Heidelberg: Springer Berlin Heidelberg. [38]. Tu, Y. J., Huang, Y. H., Strader, T. J., Subramanyam, R., Shaw, M. J. (2020). Candidate diversity and granularity in IT portfolio construction, Information Technology and Management, 21, 157-168. [39]. Wang, P., Wu, D., Chen, Z., & Wei, T. (2018). Field experience with obfuscating million‐user ios apps in large enterprise mobile development. Software Practice and Experience, 49(2), 252-273. [40]. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS quarterly, 373-396. [41]. Zhang, J., Jiang, Q., Zhang, W., Kang, L., Lowry, P., & Zhang, X. (2023). Explaining the outcomes of social gamification: a longitudinal field experiment. Journal of Management Information Systems, 40(2), 401-439.
描述 碩士
國立政治大學
資訊管理學系
112356027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112356027
資料類型 thesis
dc.contributor.advisor 杜雨儒<br>蔡瑞煌zh_TW
dc.contributor.advisor Tu, Yu-Ju<br>Tsaih, Rua-Huanen_US
dc.contributor.author (Authors) 張維暘zh_TW
dc.contributor.author (Authors) Chang, Wei-Yangen_US
dc.creator (作者) 張維暘zh_TW
dc.creator (作者) Chang, Wei-Yangen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:26:47 (UTC+8)-
dc.date.available 4-Aug-2025 14:26:47 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:26:47 (UTC+8)-
dc.identifier (Other Identifiers) G0112356027en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158574-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 112356027zh_TW
dc.description.abstract (摘要) 隨著無人機技術的迅速普及，各類控制應用程式在行動平台上大量湧現，使使用者能夠便利地透過手機操作無人機。然而，在應用商店中，部分標榜特定品牌名稱的應用實際上並非由原廠官方上架，可能造成使用者在不知情的情況下下載未經授權之應用程式，進而引發資訊安全與操作風險。 為因應此問題，本研究建立一套自動辨識架構，分析使用者對無人機應用程式的評論內容，以判斷該應用是否具有官方來源特徵。研究資料來自公開平台上的用戶評論與相關資訊頁面，並透過語言特徵建構分類模型，辨識應用程式在「官方／非官方」與「真實／假冒」兩種維度上的可能性。此外，本研究亦設計問卷實驗，驗證該分類結果是否能顯著影響使用者對應用程式的信任、下載意願與付費傾向。zh_TW
dc.description.abstract (摘要) With the rapid proliferation of drone technology, a wide variety of control applications has emerged on mobile platforms, enabling users to operate drones conveniently via smartphones. However, some applications listed on app marketplaces, despite bearing brand-like names, are not officially released by manufacturers. This can result in users unknowingly downloading unauthorized apps, potentially posing information security and operational risks. To address this issue, this study proposes an automated identification framework that analyzes user reviews of drone control apps to assess whether the apps exhibit signs of official origin. The dataset is composed of user-generated content and metadata retrieved from public platforms such as Google Play. Based on linguistic features, classification models were trained to estimate the likelihood of an app being “official/unofficial” and “real/fake” across two dimensions. Additionally, a user experiment was conducted to evaluate whether these classification outputs significantly influence users’ trust, download intentions, and willingness to pay.en_US
dc.description.tableofcontents CHAPTER 1. Introduction 1 1.1 Background 1 1.2 Research Question 4 CHAPTER 2. Literature Review 6 2.1 Drone Control on Mobile Applications 6 2.2 Official vs. Unofficial Drone Applications 7 2.3 Risks of Using Unofficial Drone Applications 9 2.4 Theoretical Foundations for Identifying Unofficial Drone Applications 11 2.5 App Review Analysis as a Method for Identifying Unofficial Drone Apps 16 2.6 NLP for Automated Review Analysis 17 CHAPTER 3. Research Framework 20 CHAPTER 4. Prediction Results 28 CHAPTER 5. Discussion 33 5.1 Theoretical Implications of the Classification Findings 33 5.2 Supplementary User Study: Experimental Design 34 5.3 Analysis of User Response Patterns 35 CHAPTER 6. Conclusion and Future Work 37 References 41 Appendix A 46 Appendix B 48 Appendix C 50 Appendix D 52zh_TW
dc.format.extent 1314819 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112356027en_US
dc.subject (關鍵詞) 無人機操作應用程式zh_TW
dc.subject (關鍵詞) 官方來源辨識zh_TW
dc.subject (關鍵詞) 自然語言處理zh_TW
dc.subject (關鍵詞) 使用者評論分析zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 資訊安全zh_TW
dc.subject (關鍵詞) 訊號理論zh_TW
dc.subject (關鍵詞) 使用者信任zh_TW
dc.subject (關鍵詞) Drone Control applicationsen_US
dc.subject (關鍵詞) Official Source Identificationen_US
dc.subject (關鍵詞) Natural Language Processing (NLP)en_US
dc.subject (關鍵詞) User Review Analysisen_US
dc.subject (關鍵詞) Deep Learningen_US
dc.subject (關鍵詞) Information Securityen_US
dc.subject (關鍵詞) Signaling Theoryen_US
dc.subject (關鍵詞) User Trusten_US
dc.title (題名) 無人機操控應用程式之真實性檢視zh_TW
dc.title (題名) Examining the Authenticity of Drone Control Applicationsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1]. Asatiani, A., Malo, P., Nagbøl, P., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021). Sociotechnical envelopment of artificial intelligence: an approach to organizational deployment of inscrutable artificial intelligence systems. Journal of the Association for Information Systems, 22(2), 325-352. [2]. Boyd, D., Kannan, P., & Slotegraaf, R. (2019). Branded apps and their impact on firm value: a design perspective. Journal of Marketing Research, 56(1), 76-88. [3]. Bygstad, B. and Øvrelid, E. (2020). Architectural alignment of process innovation and digital infrastructure in a high-tech hospital. European Journal of Information Systems, 29(3), 220-237. [4]. Chen, H., He, D., Zhu, S., & Yang, J. (2017). Toward detecting collusive ranking manipulation attackers in mobile app markets., 58-70. [5]. Chiu, C. and Huang, H. (2015). Examining the antecedents of user gratification and its effects on individuals’ social network services usage: the moderating role of habit. European Journal of Information Systems, 24(4), 411-430. [6]. Cram, W., Brohman, M., & Gallupe, R. (2015). Hitting a moving target: a process model of information systems control change. Information Systems Journal, 26(3), 195-226. [7]. Cram, W., Brohman, M., & Gallupe, R. (2016). Information systems control: a review and framework for emerging information systems processes. Journal of the Association for Information Systems, 17(4), 216-266. [8]. Davison, R. (2016). Transition arrangements to a new editorial structure. Information Systems Journal, 27(1), 1-3. [9]. Fu, S., Cai, Z., Lim, E., Liu, Y., Tan, C., Lin, Y., … & Deng, S. (2023). Unraveling the effects of mobile application usage on users’ health status: insights from conservation of resources theory. Journal of the Association for Information Systems, 24(2), 452-489. [10]. Ghazawneh, A. and Henfridsson, O. (2012). Balancing platform control and external contribution in third‐party development: the boundary resources model. Information Systems Journal, 23(2), 173-192. [11]. Karunanayake, N., Rajasegaran, J., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2020). A multi-modal neural embeddings approach for detecting mobile counterfeit apps: a case study on google play store. Ieee Transactions on Mobile Computing, 1-1. [12]. Karwatzki, S., Trenz, M., & Veit, D. (2022). The multidimensional nature of privacy risks: conceptualisation, measurement and implications for digital services. Information Systems Journal, 32(6), 1126-1157. [13]. Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus. Information Systems Journal, 25(6), 607-635. [14]. Keith, M., Babb, J., Lowry, P., Furner, C., & Abdullat, A. (2015). The role of mobile‐computing self‐efficacy in consumer information disclosure. Information Systems Journal, 25(6), 637-667. [15]. Kisembo, I., Ocen, G., Bongomin, O., Alunyu, A., Nibikora, I., Matovu, D., … & Bwire, F. (2021). An algorithm for improvement of email security on android operating system in the era of industry 4.0... [16]. Lehrer, C., Wieneke, A., Brocke, J., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems, 35(2), 424-460. [17]. Lin, Y., Chen, H., Brown, R., Li, S., & Yang, H. (2017). Healthcare predictive analytics for risk profiling in chronic care: a bayesian multitask learning approach. Mis Quarterly, 41(2), 473-495. [18]. Mettler, T. and Wulf, J. (2018). Physiolytics at the workplace: affordances and constraints of wearables use from an employee's perspective. Information Systems Journal, 29(1), 245-273. https://doi.org/10.1111/isj.12205 [19]. Miah, S., Gammack, J., & Hasan, N. (2017). Extending the framework for mobile health information systems research: a content analysis. Information Systems, 69, 1-24. [20]. Mishra, S. and Rana, G. (2019). Economics of counterfeit products: with special reference to mobile phones & watches. Theoretical Economics Letters, 09(05), 1699-1716. [21]. Murungi, D., Wiener, M., & Marabelli, M. (2019). Control and emotions: understanding the dynamics of controllee behaviours in a health care information systems project. Information Systems Journal, 29(5), 1058-1082. [22]. Ofe, H. and Sandberg, J. (2022). The emergence of digital ecosystem governance: an investigation of responses to disrupted resource control in the swedish public transport sector. Information Systems Journal, 33(2), 350-384. [23]. Porter, A. and Hooff, B. (2020). The complementarity of autonomy and control in mobile work. European Journal of Information Systems, 29(2), 172-189. [24]. Pujol, J., Arias, M., & Arratia, A. (2016). Geosrs: a hybrid social recommender system for geolocated data. Information Systems, 57, 111-128. [25]. Rajasegaran, J., Karunanayake, N., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2019). A multi-modal neural embeddings approach for detecting mobile counterfeit apps.. [26]. Rowe, F., Ngwenyama, O., & Richet, J. (2020). Contact-tracing apps and alienation in the age of covid-19. European Journal of Information Systems, 29(5), 545-562. [27]. Salo, M. and Frank, L. (2015). User behaviours after critical mobile application incidents: the relationship with situational context. Information Systems Journal, 27(1), 5-30. [28]. Sanyal, P., Menon, N., & Siponen, M. (2021). An Empirical Examination of the Economics of Mobile Application Security. MIS Quarterly, 45(4). [29]. Seneviratne, S., Kolamunna, H., & Seneviratne, A. (2015). A measurement study of tracking in paid mobile applications.. [30]. Shuraida, S., Gao, Q., Safadi, H., & Jain, R. (2024). The impact of feature exploitation and exploration on mobile application evolution and success. Journal of the Association for Information Systems, 25(3), 648-686. [31]. Spears, J. L., & Barki, H. (2010). User participation in information systems security risk management. Mis Quarterly, 34(3), 503. [32]. Sutanto, J., Palme, E., Tan, C., & Phang, C. (2013). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. Mis Quarterly, 37(4), 1141-1164. [33]. Tam, K., Feng, Y., & Kwan, S. (2019). The role of morality in digital piracy: understanding the deterrent and motivational effects of moral reasoning in different piracy contexts. Journal of the Association for Information Systems, 604-628. [34]. Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde, Jürgen Schupp, Gert G. Wagner (2011), Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences, Journal of the European Economic Association, Volume 9, Issue 3, 1 [35]. Tu, Y. J., & Piramuthu, S. (2024). Ethical consumerism, supply chains, and deceptions with RFID-based systems. Information & Management, 61(6), 104016. [36]. Tu, Y. J., & Piramuthu, S. (2024). Security and privacy risks in drone-based last mile delivery. European Journal of Information Systems, 33(5), 617-630. [37]. Tu, Y. J., & Shaw, M. J. (2009, December). An integrated approach to managing IT portfolio. In Workshop on E-Business (pp. 243-253). Berlin, Heidelberg: Springer Berlin Heidelberg. [38]. Tu, Y. J., Huang, Y. H., Strader, T. J., Subramanyam, R., Shaw, M. J. (2020). Candidate diversity and granularity in IT portfolio construction, Information Technology and Management, 21, 157-168. [39]. Wang, P., Wu, D., Chen, Z., & Wei, T. (2018). Field experience with obfuscating million‐user ios apps in large enterprise mobile development. Software Practice and Experience, 49(2), 252-273. [40]. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS quarterly, 373-396. [41]. Zhang, J., Jiang, Q., Zhang, W., Kang, L., Lowry, P., & Zhang, X. (2023). Explaining the outcomes of social gamification: a longitudinal field experiment. Journal of Management Information Systems, 40(2), 401-439.zh_TW