政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/128563

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/128563

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113318/144297 (79%)
造訪人次 : 51105146 線上人數 : 917

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/128563

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/128563

題名:	靜態廣告欺詐行為偵測技術研究-以 iOS 為例 Static ad fraud detection on iOS applications
作者:	黃存宇 Huang, Cun-Yu
貢獻者:	郁方 Yu, Fang 黃存宇 Huang, Cun-Yu
關鍵詞:	靜態分析廣告欺詐檢測 iOS 行動應用程式資訊安全 Static analysis Ad fraud detection iOS Mobile application Software security
日期:	2019
上傳時間:	2020-02-05 17:26:32 (UTC+8)
摘要:	手機App成為最受歡迎和占主導地位的軟體應用程式之一，應用程序開發人員從應用程序廣告中獲得了可觀的利潤。在應用程式中，以適當的方式呈現廣告對客戶和廣告商都有好處，但是在我們的研究中，卻發現各種廣告欺詐。廣告欺詐會破壞用戶體驗或廣告效果，但是開發人員可以從中獲得更多的利潤。在我們研究中，提出了一種靜態分析技術來檢查iOS應用程式上的廣告欺詐行為。我們會檢測出插頁式廣告，尺寸違反廣告，多重廣告和重疊式廣告的廣告欺詐行為。為了檢測這些違規，它需要使用應用程序中的特定參數來識別廣告API調用，通過動態調用很難檢測到，因為確切的調用及其參數取決於嵌套參數的運行時候的值。我們在iOS的可執行文件上採用靜態分析技術，通過該技術我們可以對目標函數的參數構建依賴關係圖。然後，我們對依賴關係圖進行字串分析，以呈現潛在的API調用及其對廣告欺詐違規的參數值。我們已經分析了上千個應用程序，這些應用程序由我們之前的應用程序靜態分析工具Binflow構造了控制流程圖，並發現208個應用程序使用了與廣告相關API的動態調用。我們進一步發現了70個具有插頁式廣告欺詐，48個具有尺寸違反廣告欺詐，31個具有多重廣告欺詐和19個具有重疊式廣告廣告欺詐。 While mobile applications (apps) become one of the most popular and dominant software applications, app developers (particularly for those who deliver free apps) gain considerable parts of proﬁts from advertisements on apps. Demonstrating ads on apps in a suitable way beneﬁts both customers and advertisers. Various ad frauds have been identiﬁed with which developers may gain extra beneﬁts but damage user experience or advertisement eﬀects. We present a static analysis technique to check ad frauds of iOS apps in this work. Particularly, we detect apps that have their ads against interstitial violation, size violation, multi-view and overlap violation. To detect these violations, it requires to identify advertisement API invocation with speciﬁc arguments in apps. It becomes hard to detect with dynamic invocation where exact calls and their arguments depend on runtime values of nested parameters. We adopt static ﬂow analysis techniques on iOS executable with which we build dependency graphs on parameters of target functions. We then conduct string analysis on dependency graphs to reveal potential API invocations with their argument values on ad fraud violations. We have analyzed more than one thousand apps that have their control ﬂow graphs constructed by our previous app static analysis tool Binﬂow, and found 208 apps using dynamic invocations on Ad related API calls. We further identiﬁed 70 apps having interstitial-violation ads, 48 apps having size violation ads, 31 apps having multi-view violation ads, and 19 apps having overlay violation ads.
參考文獻:	[1] M. C. Grace, W. Zhou, X. Jiang, and A.-R. Sadeghi, \\Unsafe exposure analysis of mobile in-app advertisements," in Proceedings of the Lfth ACM conference on Security and Privacy in Wireless and Mobile Networks, pp. 101{112, ACM, 2012. [2] D. Graziano, \\Android and iOS Still Rule the Mobile World;Microsoft and RIM Have Long Roads Ahead." https://www.appannie.com/en/insights/market-data/ app-advertising-spend-2021/, 2012. [3] Google, \\Google Admob." https://admob.google.com/, 2019. [4] Apple, \\Apple Search Ads." https://searchads.apple.com/, 2019. [5] Facebook, \\Facebook Ad." https://www.facebook.com/business/ads, 2019. [6] A. Mamiit, \\Google ags preinstalled malware as hidden threat on mil- lions of Android phones." https://www.digitaltrends.com/mobile/ android-phones-preinstalled-malware, 2019. [7] Google, \\Google Behavioral policies." https://support.google.com/admob/ answer/2753860, 2019. [8] B. Liu, S. Nath, R. Govindan, and J. Liu, \\fDECAFg: Detecting and characterizing ad fraud in mobile apps," in 11th fUSENIXg Symposium on Networked Systems Design and Implementation (fNSDIg 14), pp. 57{70, 2014. [9] F. Dong, H. Wang, L. Li, Y. Guo, T. F. BissyandSe, T. Liu, G. Xu, and J. Klein, \\Frauddroid: Automated ad fraud detection for android apps," in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 257{268, ACM, 2018. [10] B. Wang, F. Wu, and G. Chen, \\Placement fraud detection on smart phones: A joint crowdsourcing and data analyzing based approach," in International Conference on Mobile Ad-Hoc and Sensor Networks, pp. 163{179, Springer, 2017. [11] V. Dave, S. Guha, and Y. Zhang, \\Viceroi: Catching click-spam in search ad net- works," in Proceedings of the 2013 ACM SIGSAC conference on Computer & com- munications security, pp. 765{776, ACM, 2013. [12] \\Google AD Size." https://developers.google.com/admob/ios/banner. [13] V. Dave, S. Guha, and Y. Zhang, \\Measuring and Lngerprinting click-spam in ad networks," in Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication, pp. 175{186, ACM, 2012. [14] T. Yeh, T.-H. Chang, and R. C. Miller, \\Sikuli: using gui screenshots for search and automation," in Proceedings of the 22nd annual ACM symposium on User interface software and technology, pp. 183{192, ACM, 2009. [15] Apple, \\Apple Developer Documentation." https://developer.apple.com/ documentation/, 2019. [16] P. Z. Ian Beer, \\A very deep dive into iOS Exploit chains found in the wild." https://googleprojectzero.blogspot.com/2019/08/ a-very-deep-dive-into-ios-exploit.html, 2019. [17] W. Wang, I. L. Kim, and Y. Zheng, \\Adjust: runtime mitigation of resource abus- ing third-party online ads," in Proceedings of the 41st International Conference on Software Engineering, pp. 1005{1015, IEEE Press, 2019. [18] W. Yang, M. Prasad, and T. Xie, \\Enmobile: Entity-based characterization and analysis of mobile malware," in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), pp. 384{394, IEEE, 2018. [19] P. Z. Ian Beer, \\In-the-wild iOS Exploit Chain 1." https://googleprojectzero. blogspot.com/2019/08/in-wild-ios-exploit-chain-1.html, 2019. [20] H.Wang and Y. Guo, \\Understanding third-party libraries in mobile app analysis," in 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), pp. 515{516, IEEE, 2017. [21] D. M. Lazer, M. A. Baum, Y. Benkler, A. J. Berinsky, K. M. Greenhill, F. Menczer, M. J. Metzger, B. Nyhan, G. Pennycook, D. Rothschild, et al., \\The science of fake news," Science, vol. 359, no. 6380, pp. 1094{1096, 2018. [22] A. Kantchelian, M. C. Tschantz, S. Afroz, B. Miller, V. Shankar, R. Bachwani, A. D. Joseph, and J. D. Tygar, \\Better malware ground truth: Techniques for weighting anti-virus vendor labels," in Proceedings of the 8th ACM Workshop on Articial Intelligence and Security, pp. 45{56, ACM, 2015. [23] B. J. Kwon, J. Mondal, J. Jang, L. Bilge, and T. DumitraXs, \\The dropper effect: Insights into malware distribution with downloader graph analytics," in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1118{1129, ACM, 2015. [24] K. Chen, X. Wang, Y. Chen, P. Wang, Y. Lee, X. Wang, B. Ma, A. Wang, Y. Zhang, and W. Zou, \\Following devil`s footprints: Cross-platform analysis of potentially harmful libraries on android and ios," in 2016 IEEE Symposium on Security and Privacy (SP), pp. 357{376, IEEE, 2016. [25] X. Liao, K. Yuan, X.Wang, Z. Pei, H. Yang, J. Chen, H. Duan, K. Du, E. Alowaisheq, S. Alrwais, et al., \\Seeking nonsense, looking for trouble: Efficient promotional- infection detection through semantic inconsistency search," in 2016 IEEE Symposium on Security and Privacy (SP), pp. 707{723, IEEE, 2016. [26] S. Roy, J. DeLoach, Y. Li, N. Herndon, D. Caragea, X. Ou, V. P. Ranganath, H. Li, and N. Guevara, \\Experimental study with real-world data for android app secu- rity analysis using machine learning," in Proceedings of the 31st Annual Computer Security Applications Conference, pp. 81{90, ACM, 2015. [27] F. Wei, S. Roy, X. Ou, et al., \\Amandroid: a precise and general inter-component data ow analysis framework for security vetting of android apps," ACM Transactions on Privacy and Security (TOPS), vol. 21, no. 3, p. 14, 2018. [28] H. Chen, H.-f. Leung, B. Han, and J. Su, \\Automatic privacy leakage detection for massive android apps via a novel hybrid approach," in 2017 IEEE International Conference on Communications (ICC), pp. 1{7, IEEE, 2017. [29] X. Pan, X. Wang, Y. Duan, X. Wang, and H. Yin, \\Dark hazard: Learning-based, large-scale discovery of hidden sensitive operations in android apps.," in NDSS, 2017. [30] A. Armando, G. Costa, A. Merlo, and L. Verderame, \\Enabling byod through secure meta-market," in Proceedings of the 2014 ACM conference on Security and privacy in wireless & mobile networks, pp. 219{230, ACM, 2014. [31] Y. Nan, M. Yang, Z. Yang, S. Zhou, G. Gu, and X. Wang, \\Uipicker: User-input pri- vacy identifcation in mobile applications," in 24th fUSENIXg Security Symposium (fUSENIXg Security 15), pp. 993{1008, 2015. [32] J. Huang, Z. Li, X. Xiao, Z. Wu, K. Lu, X. Zhang, and G. Jiang, \\fSUPORg: Precise and scalable sensitive user input detection for android apps," in 24th fUSENIXg Security Symposium (fUSENIXg Security 15), pp. 977{992, 2015. [33] Z. Qu, V. Rastogi, X. Zhang, Y. Chen, T. Zhu, and Z. Chen, \\Autocog: Measur- ing the description-to-permission ldelity in android applications," in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1354{1365, ACM, 2014. [34] R. Pandita, X. Xiao, W. Yang, W. Enck, and T. Xie, \\fWHYPERg: Towards au- tomating risk assessment of mobile applications," in Presented as part of the 22nd fUSENIXg Security Symposium (fUSENIXg Security 13), pp. 527{542, 2013. [35] P. Suciu, \\The Biggest Cybercrime Threats of 2019." https://www. ecommercetimes.com/story/85782.html, 2019. [36] A. Metwally, D. Agrawal, and A. El Abbadi, \\Detectives: detecting coalition hit in ation attacks in advertising networks streams," in Proceedings of the 16th inter- national conference on World Wide Web, pp. 241{250, ACM, 2007. [37] A. Metwally, F. Emekci, D. Agrawal, and A. El Abbadi, \\Sleuth: Single-publisher attack detection using correlation hunting," Proceedings of the VLDB Endowment, vol. 1, no. 2, pp. 1217{1228, 2008. [38] F. Yu, Y. Xie, and Q. Ke, \\Sbotminer: large scale search bot detection," in Pro- ceedings of the third ACM international conference on Web search and data mining, pp. 421{430, ACM, 2010. [39] S. A. Alrwais, A. Gerber, C. W. Dunn, O. Spatscheck, M. Gupta, and E. Osterweil, \\Dissecting ghost clicks: Ad fraud via misdirected human clicks," in Proceedings of the 28th Annual Computer Security Applications Conference, pp. 21{30, ACM, 2012. [40] T. Blizard and N. Livic, \\Click-fraud monetizing malware: A survey and case study," in 2012 7th International Conference on Malicious and Unwanted Software, pp. 67{ 72, IEEE, 2012. [41] J. Crussell, R. Stevens, and H. Chen, \\Madfraud: Investigating ad fraud in android applications," in Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pp. 123{134, ACM, 2014. [42] X. Xiao, X. Wang, Z. Cao, H. Wang, and P. Gao, \\Iconintent: automatic identi- cation of sensitive ui widgets based on icon classication for android apps," in Pro- ceedings of the 41st International Conference on Software Engineering, pp. 257{268, IEEE Press, 2019. [43] P. Wang, D. Wu, Z. Chen, and T. Wei, \\Protecting million-user ios apps with ob- fuscation: motivations, pitfalls, and experience," in 2018 IEEE/ACM 40th Interna- tional Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), pp. 235{244, IEEE, 2018. [44] A. P. Felt, M. Finifter, E. Chin, S. Hanna, and D. Wagner, \\A survey of mobile malware in the wild," in Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, SPSM `11, pp. 3{14, 2011. [45] S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, Y. L. Traon, D. Octeau, and P. McDaniel, \\Flowdroid: precise context, ow, eld, object-sensitive and lifecycle-aware taint analysis for android apps," in ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI `14, Edinburgh, United Kingdom - June 09 - 11, 2014, p. 29, 2014. [46] L. Li, T. F. Bissyande, D. Octeau, and J. Klein, \\Droidra: taming re ection to support whole-program analysis of android apps," in Proceedings of the 25th Inter- national Symposium on Software Testing and Analysis, pp. 318{329, ACM, 2016. [47] W. Enck, P. Gilbert, S. Han, V. Tendulkar, B.-G. Chun, L. P. Cox, J. Jung, P. Mc- Daniel, and A. N. Sheth, \\Taintdroid: an information- ow tracking system for real- time privacy monitoring on smartphones," ACM Transactions on Computer Systems (TOCS), vol. 32, no. 2, p. 5, 2014. [48] T. Bao, J. Burket, M. Woo, R. Turner, and D. Brumley, \\Byteweight: Learning to recognize functions in binary code," in Proceedings of the 23rd USENIX Conference on Security Symposium, SEC`14, pp. 845{860, USENIX Association, 2014. [49] X. Meng and B. P. Miller, \\Binary code is not easy," in Proceedings of the 25th International Symposium on Software Testing and Analysis, ISSTA 2016, pp. 24{35, ACM, 2016. [50] Y. Shoshitaishvili, R. Wang, C. Salls, N. Stephens, M. Polino, A. Dutcher, J. Grosen, S. Feng, C. Hauser, C. Kruegel, et al., \\Sok:(state of) the art of war: Offensive techniques in binary analysis," in 2016 IEEE Symposium on Security and Privacy (SP), pp. 138{157, IEEE, 2016. [51] T. Reinbacher and J. Brauer, \\Precise control ow reconstruction using boolean logic," in Proceedings of the Ninth ACM International Conference on Embedded Soft- ware, EMSOFT `11, pp. 117{126, ACM, 2011. [52] D. Brumley, I. Jager, T. Avgerinos, and E. J. Schwartz, \\BAP: A binary analysis platform," in Computer Aided Verication - 23rd International Conference, CAV 2011, Snowbird, UT, USA, July 14-20, 2011. Proceedings, pp. 463{469, 2011. [53] Dynist, \\Dynist: Tools for binary instrumentation, analysis, and modication." https://github.com/dyninst. [54] D. Song, D. Brumley, H. Yin, J. Caballero, I. Jager, M. G. Kang, Z. Liang, J. New- some, P. Poosankam, and P. Saxena, \\Bitblaze: A new approach to computer security via binary analysis," in Proceedings of the 4th International Conference on Informa- tion Systems Security, ICISS `08, pp. 1{25, 2008. [55] Y. Lee, X. Wang, K. Lee, X. Liao, X. Wang, T. Li, and X. Mi, \\Understanding ios-based crowdturng through hidden fUIg analysis," in 28th fUSENIXg Security Symposium (fUSENIXg Security 19), pp. 765{781, 2019. [56] C. Xiao, \\Pirated iOS App Stores Client Successfully Evaded Ap- ple iOS Code Review." https://unit42.paloaltonetworks.com/ pirated-ios-app-stores-client-successfully-evaded-apple-ios-code-review/, 2016. [57] N. Statt, \\This illicit iPhone app store has been hiding in plain sight." https://www.theverge.com/2019/2/20/18232140/ apple-tutuapp-piracy-ios-apps-developer-enterprise-program-misuse, 2019. [58] C.-H. Lin, F. Yu, J.-H. R. Jiang, and T. Bultan, \\Static detection of api call vulner- abilities in ios executables," in 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion), pp. 394{395, IEEE, 2018. [59] M. Egele, C. Kruegel, E. Kirda, and G. Vigna, \\Pios: Detecting privacy leaks in ios applications.," in NDSS, 2011. [60] T.Werthmann, R. Hund, L. Davi, A.-R. Sadeghi, and T. Holz, \\Psios: bring your own privacy & security to ios devices," in Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security, pp. 13{24, ACM, 2013. [61] L. Davi, A. Dmitrienko, M. Egele, T. Fischer, T. Holz, R. Hund, S. Nurnberger, and A.-R. Sadeghi, \\Moc: A framework to mitigate control- ow attacks on smart- phones.," in NDSS, 2012. [62] Z. Deng, B. Saltaformaggio, X. Zhang, and D. Xu, \\iris: Vetting private api abuse in ios applications," in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 44{56, ACM, 2015. [63] F. Yu, Y.-C. Lee, S. Tai, and W.-S. Tang, \\Appbeach: Characterizing app behaviors via static binary analysis," in Proceedings of the 2013 IEEE Second International Conference on Mobile Services, p. 86, IEEE Computer Society, 2013. [64] Z. R. Fang, S. W. Huang, and F. Yu, \\Appreco: Behavior-aware recommendation for ios mobile applications," in 2016 IEEE International Conference on Web Services (ICWS), pp. 492{499, June 2016. [65] A. S. Christensen, A. Mller, and M. I. Schwartzbach, \\Precise analysis of string expressions," in Proc. 10th International Static Analysis Symposium (SAS), vol. 2694 of LNCS, pp. 1{18, Springer-Verlag, June 2003. Available from http://www.brics.dk/JSA/. [66] C. Gould, Z. Su, and P. Devanbu, \\Static checking of dynamically generated queries in database applications," in Software Engineering, 2004. ICSE 2004. Proceedings. 26th International Conference on, pp. 645{654, IEEE, 2004. [67] P. A. Abdulla, M. F. Atig, Y.-F. Chen, L. Holk, A. Rezine, P. Rummer, and J. Sten- man, \\String constraints for verication," in International Conference on Computer Aided Verication, pp. 150{166, Springer, 2014. [68] A. Das, S. K. Lahiri, A. Lal, and Y. Li, \\Angelic verication: Precise verication modulo unknowns," in International Conference on Computer Aided Verication, pp. 324{342, Springer, 2015. [69] J. Schutte and D. Titze, \\lios: Lifting ios apps for fun and prot," 2019. [70] \\Hex-Rays Decompiler Manual." https://www.hex-rays.com/products/ decompiler/manual/tricks.shtml. [71] J. Webber, \\A programmatic introduction to neo4j," in Proceedings of the 3rd an- nual conference on Systems, programming, and applications: software for humanity, pp. 217{218, ACM, 2012. [72] Facebook, \\Facebook Infer: Linters bug types-Unavailable api in supported ios sdk ." https://fbinfer.com/docs/linters-bug-types.html#UNAVAILABLE_API_ IN_SUPPORTED_IOS_SDK, 2019. [73] C. Calcagno, D. Distefano, J. Dubreil, D. Gabi, P. Hooimeijer, M. Luca, P. OHearn, I. Papakonstantinou, J. Purbrick, and D. Rodriguez, \\Moving fast with software verication," in NASA Formal Methods Symposium, pp. 3{11, Springer, 2015. [74] D. Distefano, P. W. Ohearn, and H. Yang, \\A local shape analysis based on separation logic," in International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 287{302, Springer, 2006. [75] C. Calcagno, D. Distefano, P. W. Ohearn, and H. Yang, \\Compositional shape anal- ysis by means of bi-abduction," Journal of the ACM (JACM), vol. 58, no. 6, p. 26, 2011. [76] J. Berdine, C. Calcagno, and P. W. Ohearn, \\Smallfoot: Modular automatic assertion checking with separation logic," in International Symposium on Formal Methods for Components and Objects, pp. 115{137, Springer, 2005. [77] P. Cousot, \\Abstract interpretation in a nutshell," howpublished, 7th October, 2012. [78] Facebook, \\Facebook Infer: linters.al." https://github.com/facebook/infer/ blob/472f155a7a1a5afa95f46d4300137e58cb1fa643/infer/lib/linter_rules/ linters.al, 2019. [79] Facebook, \\Facebook Infer: cPredicates.ml." https://github.com/facebook/ infer/blob/86140581d5e8690ac8ba82965aaa9d970acbb78e/infer/src/al/ cPredicates.ml, 2019. [80] M. Pradel and K. Sen, \\Deepbugs: A learning approach to name-based bug detec- tion," Proceedings of the ACM on Programming Languages, vol. 2, no. OOPSLA, p. 147, 2018. [81] R. van Tonder and C. Le Goues, \\Static automated program repair for heap prop- erties," in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), pp. 151{162, IEEE, 2018. [82] M. Harman and P. O`Hearn, \\From start-ups to scale-ups: Opportunities and open problems for static and dynamic program analysis," in 2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 1{23, IEEE, 2018. [83] N. Alshahwan, X. Gao, M. Harman, Y. Jia, K. Mao, A. Mols, T. Tei, and I. Zorin, \\Deploying search based software engineering with sapienz at facebook," in Interna- tional Symposium on Search Based Software Engineering, pp. 3{45, Springer, 2018. [84] Facebook, \\Facebook Infer: AL-examples." https://fbinfer.com/docs/linters. html#examples, 2019. [85] Apple, \\App Store Review Guidelines." https://developer.apple.com/ app-store/review/guidelines, 2019. [86] ARM, \\ARM Information Center." http://infocenter.arm.com/help/index.jsp, 2009. [87] soslab nccu, \\Github: Static Ad Fraud Detection on iOS Applications." https: //github.com/soslab-nccu/detect-adfraud, 2019. [88] soslab nccu, \\Github: BinFlow-Static Detection of API Call Vulnerabilities in iOS Executables." https://github.com/soslab-nccu/binflow, 2018. [89] C. Y. Huang, \\Video link of App 1077052682." https://drive.google.com/drive/ folders/1ep4RiMFPcL4CbfY05ZGc11UMAYGqHkA3?usp=sharing, 2019.
描述:	碩士國立政治大學資訊管理學系 106356036
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106356036
資料類型:	thesis
DOI:	10.6814/NCCU202000021
顯示於類別:	[資訊管理學系] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
603601.pdf	4832Kb	Adobe PDF2	166	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋