A Novel Architecture for Mitigating Botnet Threats in AI-Powered IoT Environments

Area of Science:

• Cybersecurity
• Internet of Things
• Artificial Intelligence

Background:

• The proliferation of AIoT devices presents significant security vulnerabilities.
• These devices are susceptible to exploitation for forming Botnets of Things (BoT).
• Attacks include DDoS, MitM, and malware distribution due to limited resources and weak encryption.

Purpose of the Study:

• To propose a novel multi-layered architecture for mitigating BoT threats in AIoT environments.
• To enhance the security posture of AIoT ecosystems against sophisticated cyberattacks.
• To provide a resilient and proactive framework for protecting AIoT assets.

Main Methods:

• Implementing a multi-layered architecture combining edge traffic inspection and sandboxing.
• Utilizing machine learning techniques for analyzing, detecting, and preventing suspicious behavior.
• Employing centralized monitoring and automated response for rapid mitigation.

Main Results:

• Demonstrated early detection of botnet activity.
• Achieved reduced false positives and improved forensic capabilities.
• Provided scalable protection for large-scale AIoT deployments, outperforming existing models.

Conclusions:

• The proposed architecture offers a comprehensive and resilient solution against BoT threats.
• The system ensures proactive and scalable protection for AIoT environments.
• This framework effectively addresses the evolving cyber threats in AIoT ecosystems.