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The Eastasouth Journal of Information System and Computer Science
Published by Eastasouth Institute
ISSN : 30266041     EISSN : 3025566X     DOI : https://doi.org/10.58812/esiscs
Core Subject : Science,
Computer Science & IT
ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by Eastasouth Institute. ESISCS aims to publish articles in the field of Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESISCS has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESISCS template and through Open Journal System (OJS) only.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 102 Documents
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AI-Driven Cyber Threat Intelligence as a Management Information System: Integrating Cybersecurity Governance and IT Project Management for Organizational Resilience Orthi, Shuchona Malek; Chakraborty, Partha; Siam, Md Abubokor; Shan-A-Alahi, Ahmed; Al Zaiem, Abdullah; Hasan, Syed Nazmul; Kaur, Jobanpreet; Mahmud, Foysal; Goffer, Mohammad Abdul
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.873

Abstract

As organizations quickly become increasingly digital, they are experiencing escalating, complex cyber threats. These challenges make cybersecurity an important area for managers and governance, and not just a technical problem. Even if you are a heavy investor in security tech, it still happens to many companies to suffer from cyber. The reasons are broken information flows, a lack of clear visibility among managers, and misalignment between the security operations and the overall decision-making. This research rethinks Artificial Intelligence (AI) based Cyber Threat Intelligence (CTI) as a Management Information System (MIS). It combines the approach of cybersecurity governance and IT project management to increase the resilience of organizations. Using MIS theory, cybersecurity governance models, and studies of IT project management, the paper derives one cohesive model for translating raw threat data into useful managerial insight. Through a design science methodology, the research chooses a lot of scholarly sources and demonstrates a layered AI-enabled CTI-MIS architecture. This is good architecture for strategic oversight, risk-based governance, and flexible project execution. The paper extends the theory of MIS to Artificial Intelligence (AI) driven cybersecurity intelligence and provides practical knowledge for companies seeking to achieve resilient digital transformation in a time of evolving cyber threats.
Performance Optimization Strategies for High-Concurrency Spring Boot Microservices in Enterprise Financial Systems Singi, Kamalakar Reddy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.883

Abstract

This paper investigates performance optimization strategies for Spring Boot–based microservices deployed in high-concurrency enterprise financial transaction systems. Although microservices improve modularity and scalability, financial workloads expose bottlenecks related to database contention, synchronous execution, and Java Virtual Machine (JVM) resource management. A coordinated, multi-layer performance optimization framework is proposed, addressing application-level, data-access-level, and runtime-level challenges. The framework is validated using a simulated high-concurrency financial transaction workload. Experimental results demonstrate improved response time, higher throughput, enhanced runtime stability, and reduced error rates under peak load conditions.

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