Research Article
A Victim-Based Framework for Telecom Fraud Analysis: A Bayesian Network Model
Table 2
Design of Bayesian network nodes for telecom fraud.
| Nodes type | Nodes (BN variables) | States of Bayesian nodes |
| Portrait | (A) Sex | (1) Male, (2) female | (B) Age | (1) Youth, (2) middle age, (3) old age | (C) Marriage | (1) Married (2) unmarried | (D) Work | (1) Company, (2) school, (3) selfemployed person, (4) government | (E) Knowledge | (1) High, (2) low |
| Fraud process | (F) Cheat type | (1) Identity fraud, (2) shopping fraud, (3) inducement fraud, (4) fictional dangerous situation fraud, (5) daily consumption fraud, (6) phishing and Trojan virus fraud, (7) other types of cheat | (G) Community type | (1) Phone, (2) message, (3) social software | (H) Suspect during cheat | (1) Yes, (2) no | (I) Call the police | (1) Yes, (2) no |
| Scam results | (J) Property loss | (1) 0–1000; (2) 1000–5000; (3) 5000–20000; (4) 20000–50000; (5) 50000+ |
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