Integrated maritime surveillance model based on artificial intelligence and blockchain for detecting illegal ship-to-ship activities

Abstract

Background: Illegal ship-to-ship (STS) activities pose a serious threat to Indonesia’s maritime security, particularly in the Riau Islands region, which is located along the strategic Malacca Strait, while conventional surveillance systems continue to face limitations due to weak data integration and the practice of Automatic Identification System (AIS) signal deactivation. This study aims to develop Maritime Guard Intelligence (MGI) as an integrated maritime surveillance system based on intelligent technology. Methods: This study employed the Research and Development (R&D) method through the stages of needs analysis, system design, concept testing, and validation of implementation potential. Findings: The proposed system integrates Isolation Forest and Convolutional Neural Network (CNN) algorithms for anomaly detection and satellite image analysis, while blockchain technology is utilized to ensure data transparency, integrity, and traceability in maritime surveillance. Conclusion: This study suggest that MGI has the potential to become an effective, participatory, and sustainable digital maritime surveillance model. Novelty/Originality of this article: The novelty of this study lies in the integration of Artificial Intelligence (AI)-based anomaly detection, satellite image analysis, and blockchain-based data verification within a single integrated maritime surveillance system.