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Title: AppReco: 基於行為識別的行動應用服務推薦系統
AppReco: Behavior-aware Recommendation for iOS Mobile Applications
Authors: 方子睿
Fang, Zih Ruei
Contributors: 郁方
Yu, Fang
Fang, Zih Ruei
Keywords: 推薦系統
Recommender System
Mobile Application
Topic Model
Date: 2016
Issue Date: 2016-08-02 16:01:36 (UTC+8)
Abstract: 在現在的社會裡,手機應用程式已經被人們接受與廣泛地利用,然而目前市面上的手機 App 推薦系統,多以使用者實際使用與回報作為參考,若有惡意行為軟體,在使用者介面後竊取使用者資料,這些推薦系統是難以查知其行為的,因此我們提出了 AppReco,一套可以系統化的推薦 iOS App 的推薦系統,而且不需要使用者去實際操作、執行 App。
整個分析流程包括三個步驟:(1) 透過無監督式學習法的隱含狄利克雷分布(Latent Dirichlet Allocation, LDA)做出主題模型,再使用增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)進行分群。(2)使用靜態分析程式碼,去找出其應用程式所執行的行為。(3)透過我們的評分公式對於這些 App,進行評分。
在分群 App 方面,AppReco 使用這些應用程式的官方敘述來進行分群,讓擁有類似屬性的手機應用程式群聚在一起;在檢視 App 方面,AppReco 透過靜態分析這些 App 的程式碼,來計算其使用行為的多寡;在推薦 App 方面,AppReco 分析類似屬性的 App 與其執行的行為,最後推薦使用者使用較少敏感行為(如使用廣告、使用個人資料、使用社群軟體開發包等)的 App。
而本研究使用在 Apple App Store 上面數千個在各個類別中的前兩百名 App 做為我們的實驗資料集來進行實驗。
Mobile applications have been widely used in life and become dominant software applications nowadays. However there are lack of systematic recommendation systems that can be leveraged in advance without users’ evaluations. We present AppReco, a systematic recommendation system of iOS mobile applications that can evaluate mobile applications without executions.
AppReco evaluates apps that have similar interests with static binary analysis, revealing their behaviors according to the embedded functions in the executable. The analysis consists of three stages: (1) unsupervised learning on app descriptions with Latent Dirichlet Allocation for topic discovery and Growing Hierarchical Self-organizing Maps for hierarchical clustering, (2) static binary analysis on executables to discover embedded system calls and (3) ranking common-topic applications from their matched behavior patterns.
To find apps that have similar interests, AppReco discovers (unsupervised) topics in official descriptions and clusters apps that have common topics as similar-interest apps. To evaluate apps, AppReco adopts static binary analysis on their executables to count invoked system calls and reveal embedded functions. To recommend apps, AppReco analyzes similar-interest apps with their behaviors of executables, and recommend apps that have less sensitive behaviors such as commercial advertisements, privacy information access, and internet connections, to users.
We report our analysis against thousands of iOS apps in the Apple app store including most of the listed top 200 applications in each category.
Reference: [1] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” the Journal of machine Learning research, vol. 3, pp. 993–1022, 2003.
[2] “Number of available applications in the google play store from decem- ber 2009 to november 2015.” number-of-available-applications-in-the-google-play-store/. (Visited on 02/22/2016).
[3]“Number of available apps in the apple app store from july 2008 to june 2015.” number-of-available-apps-in-the-apple-app-store/. (Visited on 02/22/2016).
[4] “” (Visited on 01/04/2016).
[5] B. Yan and G. Chen, “Appjoy: personalized mobile application discovery,” in Pro- ceedings of the 9th international conference on Mobile systems, applications, and services, pp. 113–126, ACM, 2011.
[6] “The sweet setup.” (Visited on 01/04/2016).
[7] “Path app under fire for unauthorized address book upload.” http://appleinsider. com/articles/12/02/07/path_app_under_fire_for_unauthorized_address_ book_upload.html. (Visited on 01/04/2016).
[8] “G data mobile malware report threat report: Q3/2015.” https://public. MobileMWR_Q3_2015_EN.pdf. (Visited on 01/04/2016).
[9] “Mcafee labs threats report november 2015.” resources/reports/rp-quarterly-threats-nov-2015.pdf. (Visited on 01/04/2016).
[10] B. Gedik and L. Liu, “Location privacy in mobile systems: A personalized anonymiza- tion model,” in Distributed Computing Systems, 2005. ICDCS 2005. Proceedings. 25th IEEE International Conference on, pp. 620–629, IEEE, 2005.
[11] A. Beach, M. Gartrell, and R. Han, “Solutions to security and privacy issues in mobile social networking,” in Computational Science and Engineering, 2009. CSE’09. International Conference on, vol. 4, pp. 1036–1042, IEEE, 2009.
[12] “Mobilead2013.” Driven-by-Facebook-Google-Mobile-Ad-Market-Soars-10537-2013/1010690. (Visited on 01/04/2016).
[13] “Gartner says mobile advertising spending will reach $18 billion in 2014.” http: // (Visited on 01/04/2016).
[14] J. Gui, S. Mcilroy, M. Nagappan, and W. G. J. Halfond, “Truth in advertising: The hidden cost of mobile ads for software developers,” in 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015, Florence, Italy, May 16-24, 2015, Volume 1, pp. 100–110, 2015.
[15] 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.
[16] “ios developer library.” (Visited on 01/04/2016).
[17] “nst/ios-runtime-headers.” (Visited on 01/04/2016).
[18] “Appbrain.” (Visited on 01/04/2016).
[19] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “Grouplens: An open architecture for collaborative filtering of netnews,” in Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW ’94, pp. 175– 186, ACM, 1994.
[20] M. J. Pazzani, J. Muramatsu, and D. Billsus, “Syskill & webert: Identifying inter- esting web sites,” in AAAI/IAAI, Vol. 1, pp. 54–61, 1996.
[21] R. Van Meteren and M. Van Someren, “Using content-based filtering for recom- mendation,” in Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47–56, 2000.
[22] M. Balabanovi ́c and Y. Shoham, “Fab: content-based, collaborative recommenda- tion,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997.
[23] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender sys- tems: A survey of the state-of-the-art and possible extensions,” Knowledge and Data Engineering, IEEE Transactions on, vol. 17, no. 6, pp. 734–749, 2005.
[24] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algo- rithms for collaborative filtering,” in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43–52, Morgan Kaufmann Publishers Inc., 1998.
[25] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, pp. 285–295, ACM, 2001.
[26] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “In- dexing by latent semantic analysis,” Journal of the American society for information science, vol. 41, no. 6, p. 391, 1990.
[27] T. Hofmann, “Probabilistic latent semantic analysis,” in Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp. 289–296, Morgan Kaufmann Publishers Inc., 1999.
[28] R. Krestel, P. Fankhauser, and W. Nejdl, “Latent dirichlet allocation for tag recom- mendation,” in Proceedings of the third ACM conference on Recommender systems, pp. 61–68, ACM, 2009.
[29] T. Hofmann, “Collaborative filtering via gaussian probabilistic latent semantic anal- ysis,” in Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR ’03, (New York, NY, USA), ACM, 2003.
[30] K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, “Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences.,” in ISMIR, vol. 6, p. 7th, 2006.
[31] L. M. de Campos, J. M. Fern ́andez-Luna, J. F. Huete, and M. A. Rueda-Morales, “Combining content-based and collaborative recommendations: A hybrid approach based on bayesian networks,” Int. J. Approx. Reasoning, vol. 51, no. 7, pp. 785–799, 2010.
[32] F. Godin, V. Slavkovikj, W. De Neve, B. Schrauwen, and R. Van de Walle, “Using topic models for twitter hashtag recommendation,” in Proceedings of the 22nd in- ternational conference on World Wide Web companion, pp. 593–596, International World Wide Web Conferences Steering Committee, 2013.
[33] T. K. Landauer and S. T. Dumais, “A solution to plato’s problem: The latent se- mantic analysis theory of acquisition, induction, and representation of knowledge.,” Psychological review, vol. 104, no. 2, p. 211, 1997.
[34] 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.
[35] W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A study of android application security.,” in USENIX security symposium, vol. 2, p. 2, 2011.
[36] 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-flow tracking system for real- time privacy monitoring on smartphones,” ACM Transactions on Computer Systems (TOCS), vol. 32, no. 2, p. 5, 2014.
[37] C. Mann and A. Starostin, “A framework for static detection of privacy leaks in android applications,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 1457–1462, ACM, 2012.
[38] M. Egele, C. Kruegel, E. Kirda, and G. Vigna, “Pios: Detecting privacy leaks in ios applications.,” in NDSS, 2011.
[39] 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.
[40] L. Davi, A. Dmitrienko, M. Egele, T. Fischer, T. Holz, R. Hund, S. Nu ̈rnberger, and A.-R. Sadeghi, “Mocfi: A framework to mitigate control-flow attacks on smart- phones.,” in NDSS, 2012.
[41] N. Nethercote and J. Seward, “Valgrind: a framework for heavyweight dynamic binary instrumentation,” in ACM Sigplan notices, vol. 42, pp. 89–100, ACM, 2007.
[42] 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.
[43] “Jgibblda:a java implementation of latent dirichlet allocation (lda) using gibbs sam- pling for parameter estimation and inference.” (Visited on 01/04/2016).
[44] T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proceedings of the Na- tional Academy of Sciences, vol. 101, no. suppl 1, pp. 5228–5235, 2004.
[45] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information processing & management, vol. 24, no. 5, pp. 513–523, 1988.
[46] “Appbeach.”, 2014. (Visited on 01/04/2016).
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