caa a22 4500 606159355 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1837-8 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1837-8 Düzenli Gürsel Department of Electronic Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey aut A new security approach for public transport application against tag cloning with neural network-based pattern recognition [Elektronische Daten] [Gürsel Düzenli] RFID tags are widely used in situations where their counterfeiting or cloning can bring financial rewards. Cloning is a particular problem because it gets round the sophisticated security measures. This paper describes a neural network-based technique for identifying cloned tickets for a public transport system. It is based on modeling passenger behavior. Cardholders' behavioral characteristics in using public transport are modeled with seven neural network model equations, one for each day of the week, and stored in an RFID card. At the time of use, these model equations or characteristics are employed to predict whether the user is the real owner of the card. Therefore, even if the RFID card is cloned, the cloned card cannot be used because a passenger's behavioral characteristics when using public transport are individual and unique, such as the passenger's signature or style of speech. Therefore, the proposed approach provides high security, especially for low-cost RFID tags. The Natural Computing Applications Forum, 2015 Artificial neural networks nationallicence Pattern recognition nationallicence Modeling nationallicence RFID nationallicence Security nationallicence Low cost nationallicence Neural Computing and Applications Springer London 26/7(2015-10-01), 1681-1691 0941-0643 26:7<1681 2015 26 521 https://doi.org/10.1007/s00521-015-1837-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1837-8 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Düzenli Gürsel Department of Electronic Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1681-1691 0941-0643 26:7<1681 2015 26 521