A Blockchain and Machine Learning-Based Integrated Framework for Dynamic Customs Security Enhancement with a Focus on Smuggling

Smuggling poses a significant challenge for global border control and customs agencies, requiring innovative solutions to counter evolving criminal tactics. This paper proposes a comprehensive approach to address these challenges. Leveraging the Hyperledger framework for blockchain ensures transparent and secure tracking of goods, while a hybrid machine learning model combining LightGBM and isolation forest enhances detection capabilities. The integration of RSA encryption, decoy documents, and a hashing combination provides a strong cryptographic framework for securing sensitive information. In the face of the sheer volume and diversity of international trade, the proposed solution aims to improve the efficiency of customs and border security by minimizing vulnerabilities in technology and internal corruption within agencies. This multifaceted approach offers a promising strategy to combat smuggling activities and strengthen the overall security of border control and customs operations globally.