Defender-Attacker Decision Tree Analysis to Combat Terrorism.

Area of Science:

• Operations Research
• Game Theory
• Security Studies

Background:

• Evaluating defensive strategies against terrorism requires dynamic models.
• Traditional game theory often assumes rational attackers, which may not apply to terrorism.
• Defender's uncertainty about attacker motivations necessitates probabilistic approaches.

Purpose of the Study:

• To develop and apply a novel methodology for analyzing defensive actions against terrorist threats.
• To assess the effectiveness of various countermeasures against diverse terrorist weapons targeting commercial aviation.
• To model deterrence, countermeasure efficacy, and adaptive attacker/defender strategies.

Main Methods:

• Defender-attacker decision tree analysis, a game-theoretic approach.
• Incorporating defender's probabilistic understanding of attacker behavior.
• Modeling deterrence, countermeasure substitution, and post-attack defensive adjustments.

Main Results:

• Not implementing countermeasures is often the optimal defensive strategy due to high costs.
• When attack probability and consequences are significant, combining DIRCMs and ground perimeter control is superior to single countermeasures.
• The model accounts for weapon/target substitution and sequential defensive decision-making.

Conclusions:

• The cost-benefit analysis of security measures is critical in counter-terrorism.
• Layered security approaches, specifically DIRCMs and perimeter control, offer enhanced protection against high-consequence terrorist attacks.
• Probabilistic modeling of attacker behavior is essential for realistic defense evaluations.