By Wen Chen Hu; Chung-Wei Lee; Weidong Kou
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Extra resources for Advances in security and payment methods for mobile commerce
Anomaly detection systems are also computationally expensive because of the overhead of keeping track of, and possibly updating several system profile metrics. Anomaly detection searches for deviations from normal user or system behavior patterns, from usage of computer or network resources. , 1990; Ryan, Lin & Miikkulainen, 1998), support vector machine method (Nguyen, 2002), information-theoretic measures (Lee & Xiang, 2001), and so forth. Table 2 shows the main idea of each anomaly detection method.
For example, buyers and sellers in eBay can rate each other after each transaction (+1, 0, -1) and the overall reputation is the sum of these ratings over the last 6 months. We can clearly see that these feedback-only metrics are flawed. A peer who has performed dozens of transactions and cheated 1 out of every 4 cases will have a steadily rising reputation in a given time duration whereas a peer who has only performed 10 transactions during the given time duration but has been completely honest will be treated as less reputable if the reputation measures are computed by a simple sum of the feedback they receive.
A peer may make false statements about another peer’s service due to jealousy or other types of malicious motives. Consequently a trustworthy peer may end up getting a large number of false statements and may be evaluated incorrectly even though it provides satisfactory service in every transaction. We introduce the credibility of feedback as a basic trust building parameter, which is equally important as the number of transactions and the feedback. The feedback from those peers with higher credibility should be weighted more than those with lower credibility.