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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah KOMPUTASI JUTIM (Jurnal Teknik Informatika Musirawas) JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Information Systems and Informatics Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Sistem Komputer dan Informatika (JSON) Journal of Information Technology Ampera Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Intechno Journal : Information Technology Journal
Ferdiansyah Ferdiansyah
Universitas Indo Global Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Other

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An Empirical Benchmarking Framework for IoT Traffic Anomaly Detection Using Elastic Stack SIEM Ferdiansyah Ferdiansyah; Reynaldi Rizki Billanivo; M Ardiansyah; Tegar Putra; M. Habibullah Amin
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2808

Abstract

Purpose: This study aims to construct a realistic IoT-MQTT benchmark dataset and evaluate supervised machine learning classifiers for detecting network traffic anomalies, specifically Distributed Denial of Service (DDoS) and spoofing attacks, within a live Security Information and Event Management (SIEM) environment. Methods: An empirical benchmarking framework based on a live Elastic (ELK) Stack SIEM environment was developed, and supervised machine learning classifiers were evaluated for IoT network traffic anomaly detection. Result: KNN and SVM achieved the highest accuracy (0.99), whereas Naive Bayes achieved 0.96. Further analysis revealed that the superior performance of KNN and SVM was largely influenced by data leakage caused by the _attacker_ip feature, while Naive Bayes demonstrated better generalization without relying on identity-based features. Conclusion: The findings highlight the importance of rigorous feature engineering and data leakage analysis when developing machine learning models for IoT traffic anomaly detection, particularly in live SIEM environments. Moreover, the proposed framework contributes to the achievement of Sustainable Development Goals (SDGs) 9 by supporting resilient digital infrastructure and secure IoT-based innovation.
Co-Authors Aan Restu Mukti, Aan Restu Adam Prasetya Andreean Dharma Arisandi Andri, A Antoni, Darius Awaludin, Aziz Billanivo, Reynaldi Rizki Briandy Tri Putra Briandy Deazwara, Muhammad Rizki Akbar Deris Stiawan Edi Surya Negara Evi Yulianingsih Fani Prasetya Fatmasari Fatmasari Gege Ardiyansyah Herferry, Ibrahim Ade Ilham budiman Ilham Ilham Kiki Rizky Nova Wardani kiky rizky nova wardani Linda Atika Linda Atika M Ardiansyah M. Ardiansyah M. Daffa Alkhussayid M. Habibullah Amin Mikko, Mikko Muhammad Ashardiansyah Putra Muhammad Haikal Mukmin, Chairul Nurwijayanti Oktarina, Tri Raja Zahilah Md Radzi Rangga Yogasuwara Reynaldi Rizki Billanivo Rezky, Muhammad Siti Hajar Othman Susan Dian Purnamasari, Susan Dian Sutra Ovi Yansa Tegar Putra Usman Ependi Valdo, Muhammad Winoto Chandra Yeni Widyanti Yesi Novaria Kunang