Fig. 1. The worm damage minimization approach.

Fig. 2. Extracted parameters of enterprise network.

Fig. 3. Fuzzy rule-based system.

Fig. 4. Basic concept of entropy minimization.

Fig. 5. The membership functions of inputs and output for damage prediction.

Fig. 6. The production rule for fuzzy decision.

Fig. 7. The decision surfaces of worm damage prediction. (a) O=1.0 (b) H=1.0 (c) T=1.0.

Fig. 8. The relation of network full infection time and trust.

Fig. 9. Fuzzy feedback control system.

Fig. 10. The membership functions of inputs and output for fuzzy feedback control system.

Fig. 11. The production rules for fuzzy feedback control system.

Fig. 12. The network full infection condition.

Fig. 13. Variation of worm damage according to (a) openness, (b) homogeneity, and (c) trust.

Fig. 14. Factor effect of classification with the change of damage threshold: (a) 0.3, (b) 0.4, (c) 0.5, and (d) 0.6.

Fig. 15. Combination of factors with the damage threshold 0.3: (a) two factors and (b) three factors.

Fig. 16. State diagram of worm attack.

Fig. 17. Fuzzy control performance in wired network: (a) full infection time with fixed trust and (b) full infection time with fuzzy control.

Fig. 18. Fuzzy control performance in wireless network: (a) full infection time with fixed trust and (b) full infection time with fuzzy control.

Fig. 19. Automated trust control framework.

The prediction accuracy RMSE (MAE)

The prediction rate and false-positive error rate