Advisor: Yeong -Sung Lin Presented by I- Ju Shih

1. Defending simple series and parallel systems with imperfect false targetsR. Peng, G. Levitin, M. Xie, S.H. Ng Advisor: Yeong-Sung Lin Presented by I-Ju Shih

2. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

3. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

4. Introduction • The classical reliability theory considers providing redundancy and improving the reliability of elements as measures of system reliability enhancement. • When survivability of systems exposed to intentional attacks is concerned such measures as protection and deploying false targets (FTs) become essential elements of the defense strategy. • Such a focus on strategic interaction between the attacker and the defender suggests a need to assume that both of them are fully strategic optimizing agents with different objectives.

5. Introduction • This paper considers the optimal defense resource distribution between two main actions available to the defender for reducing the expected damage associated with an attack: protecting the genuine system elements and deploying the false targets. • Usually the defense strategy against intentional attacks is considered for two basic system configurations: series and parallel. • This paper analyzes the optimal distribution of defense resources between protecting the genuine system elements and deploying imperfect false targets (FTs) in simple series and parallel systems.

6. Introduction • This paper analyzes a two period minmax game where the defender moves in the first period, and the attacker moves in the second period. • The defender moves first by deciding how many FTs to deploy to minimize the expected damage caused by the attacks assuming that the attacker uses the most harmful strategy by choosing the optimal number of targets to attack.

7. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

8. The model

9. The model

10. The model • Assumptions: • 1. The defender uses identical FTs with the same detection probability. • 2. The attacker can detect each FT independently of other FTs. • 3. The attacker knows the defender’s effort distribution and number of GEs and FTs and decides how many elements to attack. • 4. The attacker distributes its resources evenly among the attacked elements • 5. Each element is attacked separately. Single attack cannot destroy more than one element. • 6. In parallel system the genuine elements have identical performance. • 7. The defender distributes its protection resources evenly among the genuine elements.

11. The model • A system consists of N identical genuine elements(GEs), which are connected either in series or in parallel. • All system elements are exposed to intentional attacks. • The defender and the attacker’s resources, r and R, are fixed. • The defender distributes its resource among deploying H FTs and protecting the GEs. • Since an unprotected GE can be destroyed by an arbitrarily small but positive attack effort, the paper assumes that the defender distributes its protection resource evenly among all the GEs.

12. The model • The cost for deploying one FT is s. Therefore, the defender cannot deploy more than r/s FTs. • The FTs are imperfect. i.e. the attacker can detect each FT with probability d. If the attacker detects k FTs (with probability ) it ignores the detected FTs and attacks Qk randomly chosen elements out of N+H-k remaining undetected targets, as shown in Fig. 1.

13. The model • The vulnerability (destruction probability) of the attacked element is determined by the attacker–defender contest success function modeled with the common ratio form as When m=0, v=50%. When 0<m<1, there is a disproportional advantage of investing less than one’s opponent. When m=1, the investments have proportional impact on the vulnerability. When m>1, there is a disproportional advantage of investing more than one’s opponent. When m=∞, n is a step function where‘‘winner-takes-all’’. T>t, v=100%. T<t, v=0.

14. The model • For each k the attacker solves the optimization problem and chooses the Qk which maximizes the risk D(Qk,H). • The entire risk obtained over all possible k is • In the two period minmax game the defender moves first and solves the minmax problem: finds integer H (0<=H<=r/s) that minimizes the maximal risk given that for any H the attacker chooses the integer vector (Q0,…,QH) that maximizes the risk.

15. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

16. N genuine elements connected in series • The system consists of N GEs connected in series. • Destruction of any GE results in the destruction of the entire system. • The risk D is defined as the probability of the system destruction. • Defender’s resource = r: • H (H<=r/s) FTs are deployed, the defense effort exerted on each genuine target is t=(r-Hs)/(Na). • Attacker’s resource = R: • In the case when k (0<=k<=H) FTs are detected by the attacker, it chooses Qk (1<=Qk<=N+H-k) targets out of N+H-k undetected elements to attack and the attack effort allocated onto each target is T=R/(QkA).

17. N genuine elements connected in series • The vulnerability of each GE is where ε=A/a. • For any k and Qk the random number of attacked GEs can vary from max(0,Qk–H+k) (all FTs are attacked) to min(N,Qk) (all genuine targets are attacked). • The probability φ(Qk,i) that among Qk attacked elements i elements are the genuine ones can be obtained using the hypergeometric distribution:

18. N genuine elements connected in series • The probability that at least one out of i attacked GEs is destroyed is • The system vulnerability for any k and Qk can be obtained as • The attacker chooses the Qk which maximizes the vulnerability of the system. Thus the most harmful Qk can be expressed by

19. N genuine elements connected in series • N=5, H=5, ε=A/a=1, s=0.1, r=1

20. N genuine elements connected in series • The total expected damage to the entire system is • The defender chooses the H which minimizes D(Q0 * ,…,QH*, H), thus we have the optimal number of deployed FTs

21. N genuine elements connected in series • N=5, R=r=1, ε=A/a=1

22. N genuine elements connected in series • N=5, r=1, ε=A/a=1, s=0.05, d=0.4

23. N genuine elements connected in series • R=r=1, ε=A/a=1, s=0.05, d=0.4

24. N genuine elements connected in series • r=1, ε=A/a=1

25. N genuine elements connected in series • m=2, r=1, ε=A/a=1, N=2

26. N genuine elements connected in series • Numerical comparison • s=0.4, d=0.2, N=2, m=5, r=ε=1 • Since r/s = 1/0.4 =2, H can take only values of 0, 1, and 2.

27. N genuine elements connected in series • Numerical comparison

28. N genuine elements connected in series • Numerical comparison(R=0.5)

29. N genuine elements connected in series • Numerical comparison(R=1)

30. N genuine elements connected in series • Numerical comparison(R=2)

31. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

32. N genuine elements connected in parallel • We consider a system that is built from N identical parallel GEs with the same functionality having performance g each. • The system demand is F (F<=Ng). • The system fails to meet the demand when at least N-F/g +1 elements are destroyed. • Defender’s resource = r: • H (H<=r/s) FTs are deployed, the defense effort exerted on each genuine target is t=(r-Hs)/(Na). • Attacker’s resource = R: • In the case when k (0<=k<=H) FTs are detected by the attacker, it chooses Qk ( N-F/g +1 <=Qk<=N+H-k) targets out of N+H-k undetected elements to attack and the attack effort allocated onto each target is T=R/(QkA).

33. N genuine elements connected in parallel • The vulnerability of each GE is where ε=A/a. • For any k and Qk the random number of attacked GEs can vary from max(0, Qk–H+k) (all FTs are attacked) to min(N,Qk) (all genuine targets are attacked). • The probability φ(Qk,i) that among Qk attacked elements i elements are the genuine ones can be obtained using the hypergeometric distribution:

34. N genuine elements connected in parallel • The probability θ(i,j) that among the i attacked GEs j elements are destroyed is • Expected damage • 1. Damage proportional to the loss of demand probability • 2. Damage proportional to the unsupplied demand

35. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • If the system totally fails when the demand is not met, the expected damage is proportional to the loss of demand probability. • The demand is not met if the number of destroyed GEs j is greater than N-F/g. • In this case the risk can be obtained as the system vulnerability:

36. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • Thus the most harmful Qk can be expressed by • The total risk overall k is obtained using • The defender chooses the H which minimizes D(H), thus we have the optimal number of deployed FTs

37. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • N=8, R=r=1, ε=A/a=1, F=4, g=1

38. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • N=8, r=1, ε=A/a=1, s=0.04, d=0.4, F=4, g=1

39. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • R=r=1, ε=A/a=1, s=0.05, d=0.4, F=4, g=1

40. N genuine elements connected in parallel • 1. Damage proportional to the loss of demand probability • r=1, ε=A/a=1, F=4, g=1

41. N genuine elements connected in parallel • 2. Damage proportional to the unsupplied demand • When j GEs are destroyed the amount of unsupplied demand is equal to max(0,F-(N-j)g). • The unsupplied demand becomes positiveTheexpectedunsupplieddemand can beobtainedas • The attacker chooses • The total expected damage to the system is obtained using • Thedefender choosesthe H whichminimizes D(H):

42. N genuine elements connected in parallel • 2. Damage proportional to the unsupplied demand • N=8, R=r=1, e=1, F=4, g=1

43. N genuine elements connected in parallel • 2. Damage proportional to the unsupplied demand • N=8, r=1, ε =1, s=0.04, d=0.4, F=4, g=1

44. N genuine elements connected in parallel • 2. Damage proportional to the unsupplied demand • R=r=1, ε =1, s=0.05, d=0.4, F=4, g=1

45. N genuine elements connected in parallel • 2. Damage proportional to the unsupplied demand • F=4, g=1, r=1, ε =1

46. Agenda • Introduction • The model • N genuine elements connected in series • Numerical comparison • N genuine elements connected in parallel • Damage proportional to the loss of demand probability • Damage proportional to the unsupplied demand • Conclusion

47. Conclusion • This paper considers defending series and parallel systems against intentional attacks. • The risk for the series system is proportional to the probability of system destruction. Depending on the type of system the risk for the parallel system is either proportional to the loss of demand probability or equal to the unsupplied demand. • The paper demonstrates the methodology of analysis of optimal defense strategy as function of different parameters (number of GEs, contest intensity, total attacker’s resource).

48. Conclusion • It presents the decision curves that can be used for the making a decision about efficiency of deploying FTs depending on their cost and detection probability. • The presentedmodelusesthecontestintensityparameter m that cannotbeexactlyevaluatedinpractice. • Two ways of handling the uncertainty of the contest intensity can be outlined: 1.m can be definedasafuzzyvariableandfuzzylogicmodelcanbe studied . 2.therangeofpossiblevariationof m can be determined andthemostconservative‘‘worstcase’’defense strategy canbeobtainedundertheassumptionthat m takes the values thataremostfavorablefortheattacker.

49. Thanks for your listening.