A cost-effective adaptive repair strategy to mitigate DDoS-capable IoT botnets

Area of Science:

• Cybersecurity
• Network Security
• Internet of Things (IoT) Security

Background:

• Distributed Denial of Service (DDoS) attacks are a significant threat, often launched from large-scale Internet of Things (IoT) botnets.
• Resource constraints and dynamic states of IoT devices necessitate cost-effective and adaptive defense mechanisms against DDoS attacks.

Purpose of the Study:

• To develop a cost-effective and adaptive repair strategy (ARS) for mitigating DDoS attacks from IoT botnets.
• To establish an IoT botware propagation model that accounts for attack and defense dynamics.
• To address the ARS problem as a data-driven optimal control challenge.

Main Methods:

• An IoT botware propagation model was established to capture network state evolution under attack and defense.
• The ARS problem was framed as a data-driven optimal control problem, integrating learning and prediction of propagation parameters from network traffic data.
• An iterative algorithm, grounded in optimal control theory, was proposed to solve the problem and derive time-varying parameters and a repair strategy.

Main Results:

• The study successfully learned time-varying propagation parameters for IoT botware.
• A novel, cost-effective, and adaptive repair strategy was developed and numerically obtained.
• Computer experiments validated the performance of the learned parameters and the resulting repair strategy.

Conclusions:

• The proposed data-driven optimal control approach effectively addresses the challenge of developing adaptive repair strategies for IoT botnets.
• The developed ARS offers a promising solution for mitigating DDoS attacks in resource-constrained IoT environments.
• This research contributes to enhancing the resilience and security of IoT networks against sophisticated cyber threats.