LAUSR

Toward improving the traditional clone detection technique whose performance may be affected by dynamic changes of supply chains and misread, we present a novel and effective clone detection approach, termed double-track detection, for radio frequency identification-enabled supply chains. As part of a tag’s attributes, verification information is written into tags so that the set of all verification information in the collected tag events forms a time series sequence. Genuine tags can be differentiated from clone tags due to the discrepancy in their verification sequences which are constructed as products flow along the supply chain. The verification sequence together with the sequence formed by business actions performed during the supply chains yield two tracks which can be assessed to detect the presence of clone tags. Theoretical analysis and experimental results show that our proposed mechanism is effective, reasonable, and has a relatively high clone detection rate when compared with a leading method in this area.