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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 11 Documents
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Search results for , issue "Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)" : 11 Documents clear
Cognitive Cyber Defense: AI–MIS Integration through Big Data and Cloud Frameworks for Next-Generation Digital Resilience Hossain, Md Delwar; Sikder, Mohammad Somon; Uddin, Md Salah; Ahsan, Rezwan Moin; Uddin, Borhan; Hossen, Tawhid
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.v1i02.764

Abstract

The rapid rise in cyber threats across linked global digital ecosystems calls for a unified, intelligence-based defense strategy that brings together cybersecurity, management information systems (MIS), big-data analytics, and flexible IT governance. This study builds on the work of Kaur et al. (2023), Hasan et al. (2023), Mahmud et al. (2023), and Das et al. (2023) to create a comprehensive framework that uses artificial intelligence (AI), cloud computing, and data-driven decision-making to make digital systems more resilient. The research formulates an integrated AI–MIS Cyber-Defense Framework via a meta-synthesis of present empirical studies, clarifying the interaction among machine-learning analytics, predictive threat intelligence, and adaptive governance feedback loops. These interdependencies together improve the accuracy of detection, the ability to understand the issue in context, and the ability of organizations to adjust in unstable cyber environments. Quantitative evaluation shows that the system works better than traditional control systems. The average detection area under the curve (AUC) is over 0.93, the precision–recall metrics are above 0.90, and the composite resilience index is 27 percent higher. These results show that AI-enhanced MIS systems greatly improve cybersecurity readiness at both the national and business levels by allowing for proactive risk management, automated response coordination, and governance based on resilience. The proposed paradigm enhances the theoretical framework of cyber-resilience informatics and offers practical guidance for chief information officers (CIOs), cybersecurity strategists, and digital transformation leaders aiming to integrate scalable, self-optimizing, and AI-governed security measures into intricate digital infrastructures.

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