A new security approach for public transport application against tag cloning with neural network-based pattern recognition
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 passengers behavioral characteristics when using public transport are individual and unique, such as the passengers signature or style of speech. Therefore, the proposed approach provides high security, especially for low-cost RFID tags.
security pattern recognition rfid modeling artificial neural networks low cost