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All Journal Scientific Journal of Informatics Journal on Education JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Jurnal Tekinkom (Teknik Informasi dan Komputer) Budimas : Jurnal Pengabdian Masyarakat International Journal of Computer and Information System (IJCIS) Transformasi Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Masyarakat Bangsa Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Muhammad Abdul Muin
STMIK Bina Patria
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Optimized K-Means Clustering for Web Server Anomaly Detection Using Elbow Method and Security-Rule Enhancements Trianto, Rahmawan Bagus; Muin, Muhammad Abdul; Vikasari, Cahya
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1391

Abstract

Anomaly detection in web server environments is essential for identifying early indicators of cyberattacks that arise from abnormal request behaviors. Traditional signature-based mechanisms often fail to detect emerging or obfuscated threats, requiring more adaptive analytical approaches. This study proposes an optimized anomaly detection model using K-Means clustering enhanced with engineered security-rule features and the Elbow Method. Two datasets were used: a small dataset of 3,399 log entries from one VPS and a large dataset of 223,554 entries collected from three VPS nodes, all sourced from local production servers of the Department of Computer and Business, Politeknik Negeri Cilacap. The preprocessing pipeline includes timestamp normalization, removal of non-informative static resources, numerical feature scaling, and TF-IDF encoding of URL paths. Domain-driven security features entropy scores, encoded-payload indicators, abnormal status-code ratios, and request-rate deviations were integrated to improve anomaly separability. Experiments across five model configurations show that combining larger datasets with rule-based features significantly enhances clustering performance, achieving a Silhouette Score of 0.9136 and a Davies–Bouldin Index of 0.4712. The results validate the effectiveness of incorporating security-rule engineering with unsupervised learning to support early-warning threat detection in web server environments.
Co-Authors Cahya Vikasari, Cahya Dwi Astuti Fatimah Nur Arifah Fatkhurrochman, Fatkhurrochman handayani, riska dwi Joko Purwanto Joko Purwanto Kanafi Kanafi Kapti . Kapti Kapti, Kapti Kartika Imam Santoso Muqorobin, Muqorobin Mustofa, Khaoirul Nahdliy, Faiz Jabar Nugroho, Adlan Rahmawan Bagus Trianto Sri Waluyo Sugeng Wahyudiono Sugeng Wahyudiono, Sugeng Sukris Sutiyatno Tri Yusnanto Wahyu Priyoatmoko Yeza Febriani Yusuf Wahyu Setiya Putra