An investigation of Zipf's Law for fraud detection (DSS#06-10-1826R(2))

Fraud risk is higher than ever before. Unfortunately, many auditors lack the expertise to deal with the related risks. The objectives of this research are to develop an innovative fraud detection mechanism on the basis of Zipf's Law. The purpose of this technique is to assist auditors in reviewing the overwhelming volumes of datasets and identifying any potential fraud records. The authors conducted Quasi-experiment research on the KDDCUP'99 benchmark intrusion detection dataset to verify the performance of the proposed mechanism. The simulation experimental results demonstrate that Zipf Analysis can assist auditors to locate the source of suspicion and further enhance the resulting audit processes.