The impacts of adaptive attacking and defending strategies on mitigation of intentional threats

– The purpose of this paper is to focus on resource allocation and information disclosure policy for defending multiple targets against intentional attacks. The intentional attacks, like terrorism events, probably cause great losses and fatalities. Attackers and defenders usually make decisions based on incomplete information. Adaptive attacking and defending strategies are considered, to study how both sides make more effective decisions according to previous fights., – A stochastic game‐theoretic approach is proposed for modeling attacker‐defender conflicts. Attackers and defenders are supposed both to be strategic decision makers and partially aware of adversary's information. Adaptive strategies are compared with different inflexible strategies in a fortification‐patrol problem, where the fortification affects the security vulnerability of targets and the patrol indicates the defensive signal., – The result shows that the intentional risk would be elevated by adaptive attack strategies. An inflexible defending strategy probably fails when facing uncertainties of adversary. It is shown that the optimal response of defenders is to adjust defending strategies by learning from previous games and assessing behaviors of adversaries to minimize the expected loss., – This paper explores how adaptive strategies affect attacker‐defender conflicts. The key issue is defense allocation and information disclosure policy for mitigation of intentional threats. Attackers and defenders can adjust their strategies by learning from previous fights, and the strategic adjustment of both sides may be asynchronous.

[1]  T. G. Lewis,et al.  A general defender-attacker risk model for networks , 2008 .

[2]  Rae Zimmerman,et al.  Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[3]  Gregory Levitin Optimal Defense Strategy Against Intentional Attacks , 2007, IEEE Transactions on Reliability.

[4]  Chiman Kwan,et al.  An Adaptive Markov Game Model for Threat Intent Inference , 2007, 2007 IEEE Aerospace Conference.

[5]  Uriel G. Rothblum,et al.  Nature plays with dice - terrorists do not: Allocating resources to counter strategic versus probabilistic risks , 2009, Eur. J. Oper. Res..

[6]  Robert Powell,et al.  Allocating Defensive Resources with Private Information about Vulnerability , 2007, American Political Science Review.

[7]  S. Skaperdas Contest success functions , 1996 .

[8]  T. Lewis Critical Infrastructure Protection in Homeland Security: Defending a Networked Nation , 2006 .

[9]  Gerald G. Brown,et al.  Defending Critical Infrastructure , 2006, Interfaces.

[10]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[11]  Gregory Levitin,et al.  Minmax defense strategy for complex multi-state systems , 2009, Reliab. Eng. Syst. Saf..

[12]  Kjell Hausken Strategic defense and attack for series and parallel reliability systems , 2008, Eur. J. Oper. Res..

[13]  J. Hirshleifer Conflict and rent-seeking success functions: Ratio vs. difference models of relative success , 1989 .

[14]  Gregory Levitin,et al.  False targets efficiency in defense strategy , 2009, Eur. J. Oper. Res..

[15]  M. Naceur Azaiez,et al.  Optimal resource allocation for security in reliability systems , 2007, Eur. J. Oper. Res..

[16]  T. Sandler,et al.  Terrorism & Game Theory , 2003 .

[17]  John A. Major Advanced Techniques for Modeling Terrorism Risk , 2002 .

[18]  Pattie Maes,et al.  Explore/Exploit Strategies in Autonomy , 1996 .

[19]  L. Shapley,et al.  Stochastic Games* , 1953, Proceedings of the National Academy of Sciences.

[20]  G. Woo Quantitative Terrorism Risk Assessment , 2002 .

[21]  Vicki M. Bier,et al.  Protection of simple series and parallel systems with components of different values , 2005, Reliab. Eng. Syst. Saf..