A Bayesian game approach for node-based attribution defense against asymmetric information attacks in IoT networks

Area of Science:

• Cybersecurity
• Game Theory
• Network Security

Background:

• Traditional Internet of Things (IoT) defenses are insufficient against advanced attribution attacks, particularly with incomplete information.
• Sophisticated attacker strategies and asymmetric information create significant challenges for IoT network security.

Purpose of the Study:

• To introduce a novel Bayesian game framework, the Node-Based Attribution Attack-Defense Bayesian Game (NAADBG) Model, for addressing attribution attacks in IoT networks.
• To develop a method for quantifying node-level attack-defense payoffs and identify optimal defense strategies through Mixed Strategy Bayesian Nash Equilibrium (MSBNE).

Main Methods:

• Development of the NAADBG Model incorporating detailed attacker and defender profiles.
• Refined payoff quantification for node-level actions within the Bayesian game.
• Analysis of the Mixed Strategy Bayesian Nash Equilibrium (MSBNE) for optimal strategy selection.

Main Results:

• The NAADBG model demonstrated significant enhancements in network defense performance.
• Optimized resource allocation and preemptive threat mitigation were achieved.
• The model provides critical insights for proactive defense strategies against attribution attacks.

Conclusions:

• The NAADBG model offers a robust framework for improving IoT security against attribution attacks.
• This approach contributes to more resilient IoT security architectures and real-time environments.
• The findings support the development of proactive and optimized defense mechanisms in IoT networks.