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All Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Suca Rusdian
Yasa Anggana College of Economics
Computer Science & IT Decision Sciences, Operations Research & Management Other

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A Data Driven Information System for Cybersecurity Vulnerability Management Qurotul Aini; Agung Rizky; Suca Rusdian; Azwani Aulia; Archa Erica
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/atm.v10i1.2600

Abstract

The rapid growth of digital infrastructures has amplified cybersecurity vulnerabilities, challenging organizations to manage risks effectively. Traditional vulnerability assessment methods, such as static scoring systems, often overlook dynamic threat information, leading to suboptimal prioritization. This study addresses the gap in existing vulnerability management approaches by introducing a data-driven framework that combines internal system data, public vulnerability databases, and external threat intelligence using predictive analytics. The proposed decision support information system employs machine learning as an analytical component to estimate the likelihood of vulnerability exploitation and support vulnerability prioritization decisions. The novelty of this approach lies in its ability to prioritize vulnerabilities not only based on technical severity but also considering the context of real-world threat activity. When benchmarked against conventional methods, this approach demonstrates superior performance in identifying exploitable vulnerabilities, improving accuracy and recall, thus optimizing resource allocation. By adopting a proactive, risk-based strategy, the framework prioritizes the most critical vulnerabilities in complex IT environments. The results highlight the potential of predictive models in enhancing cybersecurity management and supporting sustainable infrastructure, driving a shift toward more efficient, data-driven decision-making.  
Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks Aswadi Jaya; Suca Rusdian; Fitra Putri Oganda; Tuti Nurhaeni; John Edwards
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kz2w4z84

Abstract

The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.
Co-Authors Agung Rizky Archa Erica Aswadi Jaya Azwani Aulia Fitra Putri Oganda John Edwards Qurotul Aini Tuti Nurhaeni