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All Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Agung Lorenzo
Eduaward Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Other

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Artificial Intelligence and Big Data Framework for Cybersecurity Resilience in Distributed Networks Syaifuddin Syaifuddin; Ahmad Gunawan; Maulana Arif Komara; Agung Lorenzo
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kjn69m96

Abstract

The rapid expansion of distributed computer networks, driven by cloud computing, IoT ecosystems, edge computing, and software-defined infrastructures, has increased cybersecurity complexity. The growing volume, velocity, and variety of network data challenge traditional security mechanisms that focus primarily on threat detection, often neglecting system resilience, adaptive response, and recovery. This study develops a resilience-oriented intelligent big data analytics framework integrating Artificial Intelegence (AI), big data processing, and distributed cybersecurity monitoring to strengthen resilience in modern digital environments. A qualitative approach was employed through systematic literature review, conceptual modeling, thematic synthesis, and comparative analysis of existing architectures. The framework consists of four interconnected layers: data acquisition and aggregation, big data processing, intelligent analytics, and adaptive response and recovery. It supports continuous monitoring, anomaly detection, threat prediction, automated mitigation, and recovery orchestration. Comparative analysis indicates that prior studies focus mainly on improving intrusion detection or machine learning techniques, providing limited attention to resilience dimensions such as adaptability, fault tolerance, recovery efficiency, and operational stability. In contrast, the proposed framework integrates intelligent analytics with scalable big data infrastructures and distributed security mechanisms to create a unified resilience-oriented cybersecurity ecosystem. Findings suggest that combining AI-driven analytics, distributed processing, and adaptive security orchestration provides a strategic foundation for enhancing cybersecurity resilience, supporting sustainable digital infrastructure development, and ensuring operational stability in increasingly complex and interconnected network environments.
Security and Privacy Enhancement in Decentralized Digital Data Sharing Environments Muhamad Yusup; Mardiana; Ageng Setiani Rafika; Otniel Feliks Putra Wahyudi; Agung Lorenzo
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vp5s1m02

Abstract

This study examines the suitability of the IPFS as a decentralized architecture for secure digital data exchange. Traditional centralized protocols, such as HTTP, introduce structural vulnerabilities, including single points of failure, metadata exposure, and susceptibility to interception or unauthorized modification. As digital data exchange becomes increasingly essential in various sectors, ensuring data security and privacy has become a growing concern. The primary objective of this study is to evaluate IPFS’s ability to address these vulnerabilities and enhance the security and privacy of digital data-sharing environments. This research employs a structured literature review to synthesize findings from distributed-systems research, cryptographic studies, and peer-to-peer networking analyses. Additionally, the study benchmarks IPFS against traditional storage protocols, such as HTTP and FTP, to assess its advantages and limitations. The results demonstrate that IPFS offers significant advantages, including content-addressed storage, Merkle-DAG verification, and decentralized peer replication. These features improve fault tolerance, ensure data integrity, and reduce the risks of data tampering. However, limitations, such as content availability and reliance on node uptime, are also noted. While IPFS is not a complete security solution, it provides a strong foundational architecture for privacy-preserving, distributed data-sharing workflows when paired with complementary cryptographic and governance frameworks, making it a viable alternative for secure digital data exchange.
Challenges and Opportunities in Implementing Big Data for Small and Medium Enterprises (SMEs) Dwi Cahyono; Apriani Sijabat; Muktar Bahruddin Panjaitan; Dwi Julianingsih; Agung Lorenzo
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.74

Abstract

Small and Medium Enterprises (SMEs) play a crucial role in the global economy but often face significant challenges when adopting new technologies like Big Data. While Big Data offers opportunities for improving decision-making, operational efficiency, and gaining a competitive edge, many SMEs struggle due to financial constraints, limited technical expertise, and concerns over data security and privacy. This paper explores the challenges SMEs encounter in adopting Big Data and identifies the opportunities it provides for growth and innovation. A mixed-methods approach is employed, combining qualitative interviews with SME managers and quantitative surveys from 150 SMEs to gather comprehensive data. The findings reveal that SMEs face barriers such as high implementation costs and lack of skilled personnel, but they also recognize the potential for Big Data to enhance customer insights, improve business processes, and foster new business models. Recommendations include exploring cost-effective solutions, investing in employee training, strengthening data security, and adopting modular systems that integrate easily with existing operations. This study underscores the importance of overcoming these challenges and leveraging Big Data as a key driver of digital transformation for SMEs, ultimately helping them to compete more effectively in an increasingly data-driven marketplace.
Co-Authors Ahmad Gunawan Apriani Sijabat Dwi Cahyono Dwi Julianingsih Mardiana Maulana Arif Komara Muhamad Yusup Muktar Bahruddin Panjaitan Otniel Feliks Putra Wahyudi Rafika, Ageng Setiani Syaifuddin Syaifuddin