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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JUITA : Jurnal Informatika
Anni Karimatul Fauziyyah
Universitas Gadjah Mada
Computer Science & IT Engineering

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Enhancing Cybersecurity: Design of an Automated Penetration Testing Framework for Common Vulnerabilities and Exposures (CVE) Nur Rohman Rosyid; Anni Karimatul Fauziyyah; Yoan Navie Ananda
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.26938

Abstract

The progression of digital transformation has increased cybersecurity concerns, primarily due to the growing prevalence of system vulnerabilities. Penetration testing (pentesting) is an essential technique for identifying and assessing vulnerabilities; however, conventional methods are labor-intensive and heavily reliant on expert participation. This study proposes the development of an automated penetration testing framework that utilizes Common Vulnerabilities and Exposures (CVE) to enhance efficiency and reduce reliance on manual processes. The framework utilizes software engineering design patterns, namely the Template Method and Abstract Factory, to guarantee modularity, scalability, and maintainability. The implementation and evaluation reveal the system's capacity to reliably perform CVE-based penetration testing activities with consistent performance across multiple iterations. Comparative testing demonstrates that the suggested framework attains superior consistency in execution time and resource utilization compared to monolithic solutions. In conclusion, the established methodology offers a dependable basis for automated CVE-based security evaluations and facilitates continuous adaptation to forthcoming cybersecurity issues.
Web-based geothermal drilling stuck pipe prediction using decision tree algorithm Rosyihan Muhtadlor; Nur Rohman Rosyid; Anni Karimatul Fauziyyah; Lalu Hendra Permana Setiawan; Irfan Saputra; Pavel Stasa; Filip Benes; Muhammad Syafrudin; Ganjar Alfian
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp604-614

Abstract

In geothermal drilling operations, data from rig-mounted sensors play a crucial role in maintaining operational efficiency and preventing drilling failures. However, sensor uncertainties and complex subsurface conditions can lead to stuck pipe incidents, causing significant non-productive time and financial losses. This study proposes web-based drilling monitoring system integrated with machine learning (ML) to predict stuck pipe occurrences in geothermal drilling. Several ML algorithms—decision tree (DT), random forest (RF), naïve Bayes (NB), multilayer perceptron (MLP), and support vector machine (SVM)—were evaluated using geothermal drilling data from an Indonesian geothermal project conducted in 2023. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied to the training dataset. Feature selection was performed using the correlation coefficient method, and predictions were generated using a 5 minute sliding window. Among the evaluated models, the DT consistently demonstrated superior performance across multiple prediction horizons (PH), achieving an accuracy of 97.4%, precision of 98.6%, recall of 72.9%, and a ROC-AUC of 0.729 using the top five selected features. The trained model was integrated into web-based monitoring platform that provides visualization and predictive alerts. This system enables early detection and better decision-making, helping improve drilling efficiency, reduce stuck pipe risks, and enhance operational safety.
Co-Authors Filip Benes Ganjar Alfian Irfan Saputra Lalu Hendra Permana Setiawan Muhammad Syafrudin Nur Rohman Rosyid Pavel Stasa Rosyihan Muhtadlor Yoan Navie Ananda