Towards an Iterated Game Model with Multiple Adversaries in Smart-World Systems.

Area of Science:

• Cybersecurity
• Game Theory
• Smart Systems

Background:

• Smart-world systems (e.g., smart cities) face diverse cyber-attacks.
• Existing defenses often target single threats, not cooperative attacks.
• Coalitional attacks by multiple adversaries pose a significant challenge.

Purpose of the Study:

• To propose a game-theory based model for understanding and defending against coalitional cyber-attacks.
• To quantify defender capacity using the extended Iterated Public Goods Game (IPGG) model.
• To analyze adversary strategies and develop effective defense mechanisms.

Main Methods:

• Formalized a game model capturing interactions between multiple adversaries.
• Incorporated a defender strategy with a probability of detecting coalitional attacks and penalizing participants.
• Derived expected payoffs using the equalizer strategy and considered collusive strategies.
• Analyzed an extortion strategy within the extended game model.

Main Results:

• The proposed model enables defenders to significantly reduce adversary payoffs by adjusting defensive resources and penalty factors.
• The defender's strategy is effective regardless of adversary strategies (random, win-stay-lose-shift, equalizer).
• Encouraging competition among adversaries effectively combats extortion strategies and suppresses total adversary payoffs.

Conclusions:

• A game-theory approach, specifically the IPGG model, provides a robust framework for defending smart-world systems against sophisticated cyber-attacks.
• Strategic implementation of penalties and fostering inter-adversary competition are key to mitigating threats and ensuring the security of IoT-enabled systems.