You are here

CLOUD COMPUTING INTEGRATED MULTI-FACTOR AUTHENTICATION FRAMEWORK APPLICATION IN LOGISTICS INFORMATION SYSTEMS

Journal Name:

Publication Year:

Abstract (2. Language): 
As new technology enables firms to perform many daily processes easier the need of authentication and authorization process is becoming an integral part of many businesses. Also mobile applications are very popular nowadays play an important role in our lives. Such demands are not only limited to Logistics Information Systems (LIS) but many field of information system as well. In this study multi-dimensional authentication which consist of online biometric face detection integrated as cloud computing software as a Service (SaaS), Near Field Communication (NFC) card authentication, location confirmation, and temporal data confirmation are gathered together to fulfill different scenarios of authentication needs of business. Microsoft Face API (Application Program Interface, SAAS (software as a service) has been used in face recognition module of developed mobile application. The face recognition module of the mobile application has been tested with Yale Face Database. Location, temporal data and NFC card information are collected and confirmed by the mobile application for authentication and authorization. These images were tested with our facial recognition module and confusion matrices were created. The accuracy of the system after the facial recognition test was found to be 100%. NFC card, location and temporal data authentication not only further increases security level but also fulfils many business authentication scenarios successfully. To the best of our knowledge there is no other authentication model other than implemented one that has a-4-factor confirmation including biometric face identification, NFC card authentication, location confirmation and temporal data confirmation.
50
57

REFERENCES

References: 

Adalan K., (2017), Yüz Tanıma, NFC ve Ses Kontrollü Kapı Kilidi Açma Sistemi, Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi, Elektrik Elektronik Fakültesi
Antonia R., Andrea C., (2013), Identity verification through face recognition, Android smartphones and NFC, Joint Research Centre European Commission Ispra (VA), Italy, 162-163.
Assarasee P., W. Krathu, T. Triyason, V. Vanijja and C. Arpnikanondt, (2017), Meerkat: A framework for developing presence monitoring software based on face recognition, 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media)
Bowersox, D.J., Closs, D.J., & Cooper, B.M. (2002), Supply Chain Logistics Management. McGraw Hill, Burr Ridge, Boston.
Christopher, M. (1998), Logistics and Supply Chain Management: Strategies for reducing cost and improving service, (2nd Ed.), Prentice Hall, New York.
de Brito M.P., Dekker R. (2004), A Framework for Reverse Logistics. In: Dekker R., Fleischmann M., Inderfurth K., Van Wassenhove L.N. (eds), Reverse Logistics. Springer, Berlin, Heidelberg, 3-27
Deniz O., Castrillon M., Hernandez M., (2003), Face recognition using independent component analysis and support vector machines, Pattern Recognition Letters, vol. 24, pp. 2153-2157
Microsoft Face API documentation (2017) https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-... (Accessed 15.12.2017)
Cloud Computing Integrated Multi-Factor Authentication Framework Application In Logistics Information
Systems
Narol T. (2014), NFC teknolojisinin toplu ulaşımda uygulanması, Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü.
Huang R., Pavlovic V., and Metaxas D. N., (2004), A hybrid face recognition method using Markov random fields, ICPR (3) , pp. 157-160.
Pei-Ju Wu, Mu-Chen Chen, Chih-Kai Tsau, (2017) "The data-driven analytics for investigating cargo loss in logistics systems", International Journal of Physical Distribution & Logistics Management, Vol. 47 Issue: 1, pp.68-83, https://doi.org/10.1108/IJPDLM-02-2016-0061
Ramnath K. Chellappa, Paul A. Pavlou, (2002) "Perceived information security, financial liability and consumer trust in electronic commerce transactions", Logistics Information Management, Vol. 15 Issue: 5/6, pp.358-368,
Tang H., Lyu M., and King I. (2003), Face recognition committee machine, In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), pp. 837- 840, April 6-10
Tolba A. S., El-Baz A.H., El-Harby A.A., (2006), Face Recognition: A Literature Review, International Journal of Signal Processing 2;2
Yale Face Database, 2016. http://vismod.media.mit.edu/vismod/classes/mas622-00/datasets/ (Accessed 17.03.2017).

Thank you for copying data from http://www.arastirmax.com