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JCR Impact Factor: 1.102
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

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Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  3/2017 - 9

Location Authentication based on Wireless Access Point Information to Prevent Wormhole Attack in Samsung Pay

RYU, G. See more information about RYU, G. on SCOPUS See more information about RYU, G. on IEEExplore See more information about RYU, G. on Web of Science, SEO, C. See more information about  SEO, C. on SCOPUS See more information about  SEO, C. on SCOPUS See more information about SEO, C. on Web of Science, CHOI, D. See more information about CHOI, D. on SCOPUS See more information about CHOI, D. on SCOPUS See more information about CHOI, D. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (2,666 KB) | Citation | Downloads: 290 | Views: 62

Author keywords
artificial neural networks, authentication, authorization, learning (artificial intelligence), wireless networks

References keywords
indoor(12), location(6), localization(6), mobile(5), fingerprinting(5), positioning(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 71 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03009
Web of Science Accession Number: 000410369500009
SCOPUS ID: 85028521720

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This paper proposes a location authentication method to prevent wormhole payment attack in Samsung Pay. The primary feature of this method is comparing wireless Access Point (AP) information collected by the current Samsung Pay user and a wireless AP model (WM) that was created from wireless AP information (WI) sent by previous Samsung Pay users. To create the WM, an autoencoder is used. Unlike the existing location authentication techniques that use WI, our method does not require additional hardware, modification of the Point of Sale (POS) software, or any pre-requisite information such as the location coordinates of the POS. We show that the proposed location authentication technique exhibits the minimum Equal Error Rate (EER) of 2.4% in real payment environments.

References | Cited By  «-- Click to see who has cited this paper

[1] Security Technology Research Team, "Analysis on Samsung Pay service and its security features," Federal Security Agency, Korea, 2015.

[2] Apple Pay, "Apple Pay: Your wallet without the wallet," Retrieved August 22, 2015.

[3] D. Choi and Y. Lee, "Eavesdropping One-Time Tokens Over Magnetic Secure Transmission in Samsung Pay," 10th USENIX Workshop on Offensive Technologies (WOOT 16), 2016.

[4] P. Vincent, H. Larochelle, Y. Bengio, and P. A. Mangzagol, "Extracting and composing robust features with denoising autoencoders," in Proc. the 25th international conference on Machine learning, ACM, Jul. 2008, pp. 1096-1103.

[5] Y. Sheng, K. Tan, G. Chen, D. Kotz, and A. Campbell, "Detecting 802.11 MAC layer spoofing using received signal strength," in INFOCOM 2008. The 27th Conference on Computer Communications, Apr. 2008, pp. 2441-2449.

[6] H. Takamizawa and N. Tanaka, "Authentication system using location information on iPad or smartphone," International Journal of Computer Theory and Engineering, vol. 4, no. 2, pp. 153-157, 2012.

[7] D. Kim, S. Kim, D. Choi, and S. Jin, "Accurate Indoor Proximity Zone Detection Based on Time Window and Frequency with Bluetooth Low Energy," Procedia Computer Science, vol. 56, pp. 88-95, 2015.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 20]

[8] M. H. Chen and C. H. Chen, "Secondary user authentication based on mobile devices location," 2010 IEEE Fifth International Conference on Networking, Architecture and Storage (NAS), Jul. 2010, pp. 277-281.
[CrossRef] [SCOPUS Times Cited 3]

[9] T. N. Lin, S. H. Fang, W. H. Tseng, C. W. Lee, and J. W. Hsieh, "A group-discrimination-based access point selection for WLAN fingerprinting localization," IEEE Transactions on Vehicular Technology, vol. 63. no. 8, pp. 3967-3976, Oct. 2014.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 43]

[10] K. Kaemarungsi and P. Krishnamurthy, "Analysis of WLAN's received signal strength indication for indoor location fingerprinting," Pervasive and Mobile Computing, vol. 8, no. 2, pp. 292-316, 2012.
[CrossRef] [Web of Science Times Cited 118] [SCOPUS Times Cited 158]

[11] N. Alsindi, Z. Chaloupka, N. AlKhanbashi, and J. Aweya, "An empirical evaluation of a probabilistic RF signature for WLAN location fingerprinting," IEEE Transactions on Wireless Communications, vol. 13, no. 6, pp. 3257-3268, Jun. 2014.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 29]

[12] Y. Jiang, X. Pan, K. Li, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang, "Ariel: Automatic Wi-Fi based room fingerprinting for indoor localization," in Proc. the 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 441-450.
[CrossRef] [SCOPUS Times Cited 125]

[13] N. Chang, R. Rashidzadeh, and M. Ahmadi, "Robust indoor positioning using differential Wi-Fi access points," IEEE Transactions on Consumer Electronics, vol. 53, no. 3, pp. 1860-1867, 2010.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 91]

[14] C. Wu, Z. Tang, Y. Liu, and W. Xi, "WILL: Wireless indoor localization without site survey," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 4, pp. 839-848, 2012.
[CrossRef] [Web of Science Times Cited 250] [SCOPUS Times Cited 295]

[15] P. Yang, W. Wu, M. Moniri, and C. C. Chibelushi, "Efficient object localization using sparsely distributed passive RFID tags," IEEE Transactions on Industrial Electronics, vol. 60, no. 12, pp. 5914-5924, Dec. 2013.
[CrossRef] [Web of Science Times Cited 115] [SCOPUS Times Cited 131]

[16] E. DiGiampaolo and F. Martinelli, "Mobile robot localization using the phase of passive UHF RFID signals," IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 365-376, Jan.2014.
[CrossRef] [Web of Science Times Cited 108] [SCOPUS Times Cited 129]

[17] F. Seco, C. Plagemann, A. R. Jimenez, and W. Burgard, "Improving RFID-based indoor positioning accuracy using Gaussian processes," 2010 International Conference on Indoor Positioning and Indoor Navigation, Sept. 2010, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 44]

[18] R. Faragher and R. Harle, "An analysis of the accuracy of Bluetooth low energy for indoor positioning applications," in Proc. the 27th International Technical Meeting of the Satellite Division of the Institute of Navigation, Tampa, FL, USA, Sep. 2014, pp. 201-210

[19] V. Moghtadaiee and A. G. Dempstera, "Indoor location fingerprinting using FM radio signals," IEEE Transactions on Broadcasting, vol. 60, no. 2, pp. 336-346, Jun. 2014.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 55]

[20] S. P. Tarzia, P. A. Dinda, R. P. Dick, and G. Memik, "Indoor localization without infrastructure using the acoustic background spectrum," in Proc. the 9th International Conference on Mobile Systems, Applications, and Services, New York, NY, USA: ACM, 2011, pp.155-168.
[CrossRef] [SCOPUS Times Cited 168]

[21] J. Chung, M. Donahoe, C. Schmandt, I. Kim, P. Razavai, and M. Wiseman, "Indoor location sensing using geo-magnetism," in Proc. the 9th international conference on Mobile systems, applications, and services, New York, NY, USA: ACM, 2011, pp. 141-154.
[CrossRef] [SCOPUS Times Cited 298]

References Weight

Web of Science® Citations for all references: 785 TCR
SCOPUS® Citations for all references: 1,589 TCR

Web of Science® Average Citations per reference: 36 ACR
SCOPUS® Average Citations per reference: 72 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-03-03 15:07 in 105 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
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