A DUAL ENCRYPTION FRAMEWORK FOR SYBIL ATTACK DETECTION IN TAX SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGY

Abstract

The Goods and Services Tax (GST) system was designed to centralize tax administration and enhance transparency and efficiency in tax governance. However, its reliance on a centralized structure makes it vulnerable to hacker attacks and operational inefficiencies. Integrating blockchain technology into GST systems offers significant opportunities for improving transparency, security, and operational performance. Despite these benefits, blockchain-based systems face critical challenges, including scalability, security, privacy, and governance issues. Among these challenges, Sybil attacks—where malicious actors create fraudulent identities to manipulate records and undermine consensus mechanisms-represent a significant threat to GST Chain systems. This paper proposes a novel attack prevention framework that employs Double Layer Attribute-Based Encryption (DL-ABE) to enhance identity verification and prevent false identity proliferation. Additionally, robust consensus mechanisms are integrated to detect and mitigate the influence of Sybil nodes, ensuring the integrity and transparency of tax-related data. The proposed framework is evaluated using a prototype developed on an open source blockchain platform. Comparative analysis demonstrates that it outperforms traditional processes in terms of security, efficiency, and reliability. By addressing these vulnerabilities, this study emphasizes the potential of blockchain-based GST systems to bridge the trust gap between on-chain and off-chain environments, paving the way for secure, scalable, and robust real-world applications.