Counterfeiting Detection in RFID-enabled Supply Chain

Counterfeiting is a generalized term which includes both the act of RFID tags, cloning and fraud. RFID tag counterfeiting attacks lead to financial losses, loss of trust and confidence in the adoption and acceptance of RFID technology. The challenge of implementing a cost-based counterfeit detection system in RFID supply chains is non-trivial because counterfeit tags exist across billions of RFID tags. In this paper, a cost-sensitive learning approach is presented. The motivation of this work is to effectively reduce the overall cost of counterfeiting attack for RFID tagged products. Experiments are conducted to present the efficiency, effectiveness, misclassification cost and test cost evaluations. Findings from this paper conclude that MetaCost learners provide the best result. However, when compared to AdaCost, MetaCost is more expensive in classifying counterfeit tags.

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