Resource distribution in multiple attacks against a single target.

Area of Science:

• Game Theory
• Operations Research
• Cybersecurity

Background:

• Sequential attack models are crucial for understanding resource allocation in adversarial scenarios.
• Contest intensity significantly influences the effectiveness of attacker and defender efforts.
• Limited resources necessitate strategic decision-making for both attackers and defenders.

Purpose of the Study:

• To analyze optimal attacker strategies in sequential attacks under varying contest intensities.
• To determine the most effective resource allocation methods for attackers (lump-sum vs. distributed).
• To compare different distribution patterns (equal, increasing, decreasing) for sequential attacks.

Main Methods:

• Game-theoretic modeling of sequential contests.
• Analysis of attacker objectives: maximizing vulnerability vs. minimizing resource expenditure.
• Simulation and analytical solutions for optimal resource allocation.

Main Results:

• The optimal strategy depends heavily on contest intensity and attacker objectives.
• Distributing resources is often more effective than a single large attack, especially at high contest intensities.
• Geometrically varying resource distribution can outperform equal distribution under specific conditions.

Conclusions:

• Understanding contest intensity is key to developing effective attack and defense strategies.
• Resource allocation in sequential attacks requires a nuanced approach, moving beyond simple lump-sum strategies.
• This research provides insights for optimizing offensive and defensive operations in resource-constrained environments.