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Rusmanto, Rusmanto
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Predictive Maintenance on Fortinet Firewall Devices Using Artificial Intelligence Rustianto, April; Murobbie, Faqih; Rusmanto, Rusmanto
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7448

Abstract

The growing complexity of enterprise network infrastructures has increased the importance of predictive maintenance for network security devices, particularly firewall systems. In operational environments using Fortinet firewalls, large volumes of firewall logs are continuously generated, while existing monitoring tools such as FortiAnalyzer remain limited to descriptive analysis and lack predictive capabilities. This study aims to evaluate the effectiveness of artificial intelligence, specifically Large Language Models (LLMs), for predictive maintenance through automated analysis of firewall logs. Four open-source LLMs-Gemma 2B, Mistral 7B, DeepSeek-R1 7B, and Qwen 2.5-Coder 7B-were benchmarked using a standardized Indonesian-language prompt designed to extract high-severity events, including emergency, alert, and critical conditions, from multi-severity Fortinet log data. The evaluation focused on AI benchmarking metrics such as severity filtering compliance, reasoning accuracy, linguistic consistency, structural clarity, and processing efficiency. The results indicate that Qwen 2.5-Coder 7B provides the most reliable overall performance, demonstrating strong adherence to severity constraints, consistent Indonesian-language output, and well-structured analytical results suitable for operational predictive maintenance. Mistral shows superior contextual reasoning but exhibits language inconsistency, while Gemma offers the fastest processing time with moderate severity accuracy. DeepSeek performs least effectively due to instruction non-compliance. This study addresses an existing research gap by demonstrating how large language models can support predictive maintenance for firewall-based network security systems and provides a comparative framework for future AI-driven firewall and log-analysis research.
Co-Authors 'afina, Dindha Tri Agi Ginanjar Agustria, Weni Ali Mandan, Ali Arafah, Nurfauziah Ardiah Juita Ardiansyah, Ahmad Rio Aref Vai, Aref Aspa, Agus Prima Bayanussabil, Muhammad Syahid Davied Wahyu Wismanindra Dinan Mitsalina Fekie Adila Hertini, Etty Sri Ida Mentayani Juhariah, Jujuk Kasman Rukun Mardiansyah, Firman Margaretha Praba Aulia Muhammad Faisal Muqmiroh, Lailatul Murobbie, Faqih Pramudiawardani, Shelly Praptono, Soegardo Indra Putri Adita Wulandari Rahmadi, Catur Riga Rahmatullah, Muhammad Imam Ramadani, Desta Ramadhan, Riki Risalatul Latifah Rustianto, April Sapriandi, Sapriandi Sensusiati, Anggraini Dwi Sigit Muryanto, Sigit Sulastio, Agus Wijaya, Regi Wismanindra, Davied Wahyu