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p-Index From 2020 - 2025
0.562
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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Andi M. Yusuf
Unknown Affiliation
Computer Science & IT

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Zero Trust Architecture as a New Paradigm in Cyber Security Andi M. Yusuf; Dian Megah Sari; Hilda Ashari; Hamdy Nur Saidy; Musawwir
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8272

Abstract

The traditional perimeter-based security model has proven inadequate in addressing modern cybersecurity challenges characterized by cloud adoption, remote work, and sophisticated cyber threats. This mixed-methods study examines Zero Trust Architecture (ZTA) as an emerging paradigm that fundamentally shifts security principles from "trust but verify" to "never trust, always verify." Through systematic literature review of 156 peer-reviewed articles and analysis of 12 cross-sector implementation case studies, this research explores the core principles, implementation strategies, benefits, and challenges of ZTA adoption. Key quantitative findings demonstrate that organizations implementing ZTA achieve 67% reduction in breach costs, 48% improvement in threat detection, and 52% enhancement in incident response capabilities. However, implementation faces significant barriers including technical complexity (78% of organizations), cultural resistance (65%), and skills gaps (72%). This study contributes a novel cross-sector ZTA maturity framework and provides evidence-based insights for cybersecurity professionals and organizational leaders considering ZTA adoption.
Random Forest Implementation for Suricata-Based Real-Time DDoS Attack Detection Juhari; Nuralamsah Zulkarnaim; Muh Rafli Rasyid; Andi M. Yusuf
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8339

Abstract

The Random Forest classifier model trained on the CICDDoS2019 dataset achieved an accuracy of 99.94%, precision of 99.79%, recall of 99.94%, and F1-Score of 99.87%, demonstrating strong performance in detecting Distributed Denial of Service (DDoS) attacks. This study aims to develop a real-time DDoS detection system by integrating Suricata as an intrusion detection system (IDS) and Random Forest as a machine learning model. The Dataset used consisted of 431,371 samples and 31 selected features from the results of feature selection. The system works by monitoring log eve.json from Suricata, extracts relevant features directly, then performs classification using a trained model. Predictions are displayed via a Flask-based web interface for easy monitoring. In the live traffic test, the model gave a confidence score of 0.65 for attacks and 0.81 for normal traffic. These results prove that the built system is able to recognize DDoS attack patterns efficiently and can be applied to real network infrastructure as a real-time Threat Detection Solution.
Digital Forensics in Open Journal Systems: Case Study on Security Breach and Data Recovery Andi M. Yusuf; Dian Megah Sari; Musawwir
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9778

Abstract

Open Journal Systems (OJS) has become the dominant platform for scholarly journal publication in Indonesia, with more than 8,500 active journals in 2024. However, the growing cyber threats targeting academic infrastructures demand the development of digital forensic methodologies specifically tailored to the OJS ecosystem. This research develops a comprehensive framework for digital forensic investigation on the OJS platform through the analysis of 45 security incidents that occurred in Indonesian scholarly journals during the 2022–2024 period. The proposed Digital Forensic for Academic Publishing (DFAP) methodology covers preservation, acquisition, examination, analysis, and presentation, specifically designed for the OJS architecture. The implementation of this framework in 12 real-world cases demonstrated a success rate of 89.3% in data recovery, 76.2% in perpetrator identification, and 94.4% in operational system restoration, with an average resolution time of 72 hours. The study also identified 15 common vulnerability patterns in Indonesian OJS installations and produced 28 security recommendations that can reduce incident risks by up to 67%. The main contributions of this research include the development of OJS-specific forensic tools, the standardization of investigation procedures for academic institutions, and the establishment of the Indonesian Academic Digital Forensic Database (IADFD) as a knowledge-sharing repository.
Co-Authors Dian Megah Sari Hamdy Nur Saidy Hamdy Nur Saidy Hilda Ashari Juhari Hasan Muh Rafli Rasyid Musawwir Nuralamsah Zulkarnaim