A Secure and Trusted Communication Solution for Web 3.0 Based on Edge Intelligence

Abstract

The rise of AI has positioned edge computing as a pivotal domain for deploying machine learning technologies, fostering agile processing, and enhancing network robustness and decision-making capabilities. This paper addresses the underexplored aspects of DDoS and phishing attacks, and precise decision-making at network edge devices within blockchain-based frameworks. The contribution lies in proposing an incentive-based security mechanism to divert intruders from genuine routes. Legitimate devices conducting accurate decision-making are rewarded, enticing their participation in identifying false devices. A honeypot intrusion detection system attracts false devices, and real-time trust computation monitors communication devices. This approach is analyzed under security threats and network delays, demonstrating its efficacy compared to existing methods in safeguarding edge computing environments.