Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks.

Area of Science:

• Cybersecurity
• Internet of Things (IoT) Security
• Malware Mitigation

Background:

• Self-propagating malware poses a significant threat to IoT devices, causing widespread security breaches.
• Existing mitigation strategies, like white-hat botnets, face challenges such as resource consumption and overpopulation.
• Previous lifespan control for white-hat botnets lacked real-time adaptability.

Purpose of the Study:

• To propose a novel method for diffusing white-hat botnets by dynamically controlling their lifespan.
• To address the overpopulation issue of white-hat botnets while maintaining their efficacy against malware.
• To achieve a 'zero-botnet' situation by balancing diffusion and population control.

Main Methods:

• Implementing a dynamic lifespan control mechanism for white-hat botnets.
• Utilizing a 'ripple effect' to manage the diffusion area and population of white-hat botnets.
• Adjusting white-hat botnet lifespan based on real-time conditions of both malware and white-hat botnet congregations.

Main Results:

• The proposed method effectively reduces the overpopulation rate of white-hat botnets.
• Experimental results demonstrate a reduction in white-hat botnet numbers by approximately 80%.
• The dynamic lifespan control successfully balances the expansion and suppression of white-hat botnets.

Conclusions:

• Dynamic lifespan control is an effective strategy for managing white-hat botnets in IoT environments.
• The 'ripple effect' mechanism offers a promising approach to mitigate malware threats without causing resource exhaustion.
• This research contributes to more sustainable and efficient cybersecurity solutions for IoT ecosystems.