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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 143 Documents
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A Machine Learning-Based Intrusion Detection Framework for Enhanced Network Security Ranobir Hasan; Hira Jamal; Kamal Kamal; Khan Khan; Amit Kumar; Antu Roy
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 01 (2026): 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.v4i01.1168

Abstract

The rapid expansion of interconnected networks, cloud computing, Internet of Things (IoT) devices, and digital communication technologies has significantly increased the complexity of modern cyber threats, making traditional network security mechanisms increasingly inadequate. Intrusion Detection Systems (IDS) are essential components of cybersecurity infrastructures, designed to monitor network activities and identify malicious behavior before it compromises system integrity. However, conventional signature-based and rule-based IDS are primarily effective against previously known attack patterns and often fail to detect zero-day attacks, advanced persistent threats (APTs), and other evolving cyber threats. To address these limitations, machine learning (ML) has emerged as a transformative technology that enables adaptive, intelligent, and data-driven intrusion detection by learning complex patterns from network traffic and system behavior. This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models. The paper examines widely used benchmark datasets, feature selection and feature engineering methods, data preprocessing techniques, and commonly adopted performance evaluation metrics for assessing intrusion detection effectiveness. It also reviews various IDS deployment architectures, including centralized, distributed, edge-based, cloud-enabled, and hybrid frameworks, highlighting their strengths and limitations in different networking environments. To provide a clear understanding of intelligent intrusion detection mechanisms, the review introduces two conceptual frameworks: a machine learning-based intrusion detection pipeline that illustrates the end-to-end process from data acquisition to threat classification, and a layered network security architecture demonstrating the integration of ML techniques into modern cybersecurity infrastructures. Furthermore, the paper discusses critical challenges affecting the deployment of ML-based IDS, including data imbalance, scalability, computational complexity, model interpretability, adversarial machine learning attacks, privacy preservation, and real-time processing constraints.
Intent-Aware Enterprise Architecture Using Persistent AI Agents for Dynamic FinTech Service Composition Nithesh Gudipuri
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): 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.v2i01.1170

Abstract

As financial technology (FinTech) ecosystems grow increasingly complex, the rigidity of traditional service-focused enterprise architectures (EA) becomes a problem in the face of changing customer intent, diverse data sources and shifting regulatory requirements. This paper gathers insights from twenty-one peer-reviewed publications over the last 14 years (2009 to 2022) and suggests an intent-aware enterprise architecture in which the persistent artificial intelligence (AI) agents continuously interpret the organizational and customer intent to orchestrate dynamic FinTech service composition. From the reviewed literature, 52.4 percent covered the use of artificial intelligence and machine learning in financial services, 38.1 percent covered the coordination of multi-agent system (MAS), and 9.5 percent covered service-oriented enterprise architecture and integration with distributed ledger. The study builds on these three streams, with a five-layer architecture: intent interpretation, persistent agent orchestration, dynamic service composition, domain services, and governance. The synthesis suggests that the explanation of these agents, which are persistent, can be used to increase the flexibility and auditability in coordination compared to traditional service-oriented designs, but also imposes new governance and security requirements. The proposed framework provides the FinTech enterprises with structured guidance on delivering adaptive and intent-driven services and supplies a researchers' framework for empirical testing and validation of the proposed framework.
Intelligent Cybersecurity Frameworks for Data Protection in Cloud-Integrated Management Information Systems Kesavan Sundara Mudaliyar; S. Satish Kumar
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): 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.v2i02.1165

Abstract

Enterprise workloads keep moving to public, private, and hybrid cloud environments, and that shift is widening the attack surface available to would-be intruders just as organizations lean harder on Management Information Systems (MIS) to run day-to-day decisions. This paper works through fifteen recent studies sitting at the crossing point of artificial intelligence (AI), cybersecurity, cloud computing, and MIS governance, and tries to say something coherent about what they add up to. Rather than proposing and testing one new tool, the review pulls out the themes that keep resurfacing, AI and machine-learning-based threat detection, cyber threat intelligence, big-data analytics, data governance, federated and privacy-preserving learning, sustainable data-center design, and the human side of security that technical papers tend to skip and organizes them into a five-layer conceptual framework meant to help later empirical work. The review follows an explicit search, screening, and synthesis process, described in Section II; each proposed framework layer is traced back, in the discussion itself, to the specific literature that motivates it, and a thematic distribution chart shows how attention is split across sub-topics in the corpus. What comes out of this is that detection capability and MIS governance are comparatively well covered, while a handful of cross-cutting issues, explainability at the implementation level, how employees behave once AI is in the loop, energy-aware security operations, and the practical limits of federated learning, are thinner than their real-world importance would suggest. The paper closes by naming its own limitations and laying out where empirical work still needs to happen.

Page 15 of 15 | Total Record : 143

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