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All Journal Journal of Renewable Energy and Smart Device Cyber Security and Network Management
Lely Priska D Tampubolon
Perbanas Institute
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Comprehensive Study on Blockchain Based Cryptographic Key Management and Secure Communication Protocols for Large Scale Cyber Physical Systems in Industrial Environments Rudolf Sinaga; Lely Priska D Tampubolon
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.12

Abstract

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.
Hybrid Rule-Based and Anomaly Detection Model for Wholesale Sales Risk Classification Dwi Atmodjo WP; Winny Purbaratri; Lely Priska D Tampubolon; Nani Krisnawaty Tachjar; Deden Prayitno; Budi Indiarto; M Iman Wahyudi
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1184

Abstract

Large-scale wholesale transaction systems face increasing risks from suspicious purchasing patterns, abnormal customer behavior, and operational inconsistencies, while conventional rule-based methods may fail to identify previously unseen patterns. This study develops a decision-level hybrid risk classification model that combines expert-derived business rules with post-hoc anomaly decisions from Isolation Forest and DBSCAN. The modules are orchestrated in Apache Airflow and executed through a scheduled daily DAG for batch transaction monitoring. The model was evaluated retrospectively using 12,476 wholesale transactions recorded over 12 months in a single company. The business rules and ground-truth labels were elicited from the same expert pool; therefore, the separation between model development and evaluation was implemented at the data level through independent training, validation, and test subsets. The Hybrid Rule + Isolation Forest configuration achieved the highest accuracy at 87.5%, compared with 74.3% for the authors' rule-based baseline. The resulting 13.1-percentage-point gain should be interpreted as an internal ablation result rather than a comparison with a state-of-the-art external model. For operational efficiency, the automated workflow processed a batch of 1,000 transactions in 42 seconds, compared with approximately 3 hours of manual processing. These findings suggest that combining interpretable business rules with anomaly detection at the decision level can improve risk classification while retaining operational transparency. However, because the evaluation used data from only one wholesale company and shared expert sources for rules and labels, validation across independent companies and expert groups is required before broader generalization.
Co-Authors Budi Indiarto Deden Prayitno Dwi Atmodjo WP M. Iman Wahyudi Nani Krisnawaty Tachjar Rudolf Sinaga Winny Purbaratri