摘要(英) |
With the development of mobile electronic payment technology becoming more and more mature, the application is also widely used in daily life such as food, clothing, housing, transportation, etc., the protection of information security threats and protection of proprietary protection are increasing day by day. Recently, an endless stream of information, a large number of leaks, and fraud For cases of bullying, ransomware and fraudulent use of identity, how to improve the user’s information security and privacy protection capabilities will be very important alternatives at present. This study uses an account-based ticketing system, hereinafter referred to as account-based ticketing, to expand the discussion. Prevent fraud and protect proprietary functions, and study how to help ABT expand and increase related modules under the reorganized transaction mechanism, and improve information security and proprietary protection capabilities. In this study, the actual case of introducing ABT in the upgrade of public transportation ticketing systems in Singapore and Dubai, discussing the problems found after its introduction, and introducing the various architectures of the current ABT in compliance with EMC regulations, through comparison, this study believes that the standard ABT EMV also proposed the FP (Fraud Privacy)-ABT architecture, adding machine learning fraud detection and the attribute-based credential set of K-AnonymityPlus through extension to study security. The contribution is to propose a new FP-ABT architecture and explain the design of internal key modules. |
參考文獻 |
網站:
[1] RFID規格書,www.chilitag.com.tw/solution_en.php?ID=165 access 3 May 2020
[2] ABT,www.linkedin.com/pulse/what-account-based-ticketing-daniel-callaway access 10
April 2020
[3]悠遊卡新聞times.hinet.net/news/22964196 access 20 July 2020
[4] NCCC工作報告書 ,www.nccc.com.tw/wps/wcm/connect/eb3ab163-eb85-4e11-b091- 7da1ec2ade79/107%E5%B9%B4%E5%BA%A6%E5%B7%A5%E4%BD%9C%E5%A0%B1%E5%91%8A%E6%9B%B8.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-eb3ab163-eb85-4e11-b091-7da1ec2ade79-mC-54B. access 20 July 2020
[5]悠遊卡wikizh.wikipedia.org/wiki/%E6%82%A0%E9%81%8A%E5%8D%A1access 22 July 2020
[6]電子錢包, hdl.handle.net/11455/92908 access 10 April 2020
[7] NFC,www.cra.org.tw/download/GetD.aspx?13 access 12 April 2020
[8]詐欺偵測 www.researchgate.net/publication/333865808_TitAnt_Online_Real-time_Transaction_Fraud_Detection_in_Ant_Financial access 12 July 2020
[9]隱私保護pure.qub.ac.uk/en/publications/privacy-preserving-electronic-ticket-scheme-with-attribute-based-
[10]RandomForest,www.researchgate.net/publication/329016339_Refined_Weighted_Random_Forest_and_Its_Application_to_Credit_Card_Fraud_Detection access 28 June 2020
[11]K-Star, citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4098 access 30 May 2020
[12]信用卡詐欺偵測 ieeexplore.ieee.org/document/8316850 access 21 March 2020
[13]貨幣演進, bestwise.com.tw/File/TeachApp/52MFF00702/ch02.pdf access 11 March 2020
[14]行動支付報告, mic.iii.org.tw/news.aspx?id=572 access 12 July 2020
[15]日本App報導www.ithome.com.tw/news/131677 access 11 July 2020
[16]NFC支付, centerforpbbefr.rutgers.edu/TaipeiPBFR&D/2013-06-01/PPT/04-3NFC%E8%A1%8C%E5%8B%95%E6%94%AF%E4%BB%98%E8%88%87%E7%AC%AC%E4%B8%89%E6%96%B9%E6%94%AF%E4%BB%98%E6%87%89%E7%94%A8%E7%9A%84%E7%99%BC%E5%B1%95.pdf access 02 July 2020
[17]悠遊卡, www-ws.gov.taipei/Download.ashx?u=LzAwMS9VcGxvYWQvMzkwL3JlbGZpbGUvMTk3MTcvMzMyNjg3OS9kODZlMTA5ZS0zYWQ0LTQyYzAtYmYxMy1kN2I5NzIxYTRjMWYucGRm&n=bTE5LnBkZg%3D%3D access 02 July 2020
[18]Light Gradient Boosting, www.dmtk.io access 18 July 2020
[19]KStar,www.researchgate.net/publication/335243928_Credit_Card_Fraud_Detection_using_k-star_Machine_Learning_Algorithm access 01 July 2020
期刊:
[20] Charl, A. O., Gerhard, P. H., “A Generic NFC-enabled Measurement System for Remote. Monitoring and Control of Client-side Equipment”, 2011 Third International Workshop
on Near Field Communication, pp. 44-49, Hagenberg, Austria, February 22, 2011.
[21] Hasoo, E., Hoonjung, L., Heekuck, O., “Conditional Privacy Preserving Security Protocol for NFC Applications”, 2013 IEEE International Conference on Consumer Electronics
(ICCE), pp.153-160, Las Vegas, NV, USA, January 11-14, 2013.
[22] Mining , K. R. Seeja and Masoumeh Zareapoor,FraudMiner: A Novel Credit Card Fraud
Detection Model Based on Frequent Itemset , Hindawi Publishing Corporation The
Scientific World Journal Volume 2014, Article ID 252797, pp.3-5, 2014.
[23] Ritika Agarwal, Dr. Barjesh Kochar, Deepesh Srivastava ,A Novel and Efficient KNN
using Modified Apriori Algorithm, International Journal of Scientific and Research
Publications, Volume 2, Issue 5, pp.2-5,2012.
[24] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proceedings of the 20th International Conference on Very Large Data Bases,
pp.487–499, 1994. |