Politeknik Dergisi 2147-9429 Gazi University 10.2339/politeknik.1379049 Machine Learning (Other) Makine Öğrenme (Diğer) Anomaly Detection with Gradient Boosting Regressor on HVAC Systems HVAC Sistemlerinde Gradyan Arttırma Regresyonu ile Anomali Tespiti https://orcid.org/0000-0003-4279-0648 Adak Muhammed Fatih SAKARYA ÜNİVERSİTESİ https://orcid.org/0000-0002-3228-7494 Kibar Refik SAKARYA UNIVERSITY https://orcid.org/0000-0002-9859-6855 Ovaz Kevser Rochester Institute of Technology of Dubai 12 12 2024 27 6 2117 2125 10 20 2023 12 12 2023 Copyright © 1998, Journal of Polytechnic 1998 Journal of Polytechnic

HVAC systems are important in buildings due to their significant energy consumption, impact on indoor air quality, and role in occupant comfort. Optimizing the operation and control of these systems is crucial for improving energy efficiency and reducing costs. Anomaly detection in HVAC systems aims to optimize energy consumption, improve thermal comfort and indoor air quality, detect and isolate sensor faults, and, more importantly, detect cyber-attacks. By analyzing system data for unusual patterns or unauthorized access attempts, anomaly detection can play a vital role in safeguarding HVAC systems against cyber threats. Detecting and isolating potential cyber-attacks can prevent disruptions in building operations, protect sensitive data, and ensure the continued functionality of HVAC systems securely and reliably. In this study, Gradient Boosting Regressor is used to improve the anomaly detection capabilities of HVAC systems. Traditional anomaly detection methods often struggle to adapt to the dynamic nature of HVAC systems and may generate false alarms or miss critical issues. To address these challenges, we propose the application of Gradient Boosting Regressor, a powerful machine learning technique, to enhance anomaly detection accuracy and reliability. We evaluate the model's performance using real-world HVAC data, comparing it with existing anomaly detection methods. The results demonstrate significant improvements in the system's ability to identify anomalies accurately while minimizing false alarms. This research advances HVAC system security by providing a more robust and adaptive anomaly detection solution. Integrating Gradient Boosting Regressor into the cybersecurity framework of HVAC systems offers improved protection against cyber threats, thereby enhancing the resilience and reliability of critical infrastructures.

HVAC sistemleri, önemli enerji tüketimleri, iç mekan hava kalitesi üzerindeki etkileri ve bina sakinlerinin konforundaki rolleri nedeniyle binalarda büyük önem taşımaktadır. Bu sistemlerin çalışmasını ve kontrolünü optimize etmek, enerji verimliliğini artırmak ve maliyetleri düşürmek için çok önemlidir. HVAC sistemlerinde anomali tespiti, enerji tüketimini optimize etmeyi, termal konforu ve iç mekan hava kalitesini iyileştirmeyi ve sensör hatalarını tespit edip izole etmeyi, ancak daha da önemlisi siber saldırıları tespit etmeyi amaçlamaktadır. Anomali tespiti, olağandışı modeller veya yetkisiz erişim girişimleri için sistem verilerini analiz ederek, HVAC sistemlerinin siber tehditlere karşı korunmasında hayati bir rol oynayabilir. Potansiyel siber saldırıların tespit edilmesi ve izole edilmesi, bina operasyonlarındaki kesintileri önleyebilir, hassas verileri koruyabilir ve HVAC sistemlerinin güvenilir bir şekilde işlevselliğini sürdürmesini sağlayabilir. Bu çalışmada, HVAC sistemlerinin anomali tespit yeteneklerini geliştirmek için Gradyan Arttırma Regresyonu kullanılmıştır. Geleneksel anomali tespit yöntemleri genellikle HVAC sistemlerinin dinamik yapısına uyum sağlamakta zorlanır ve yanlış alarmlar üretebilir veya kritik sorunları gözden kaçırabilir. Bu zorlukların üstesinden gelmek için, anomali tespit doğruluğunu ve güvenilirliğini artırmak üzere güçlü bir makine öğrenimi tekniği olan Gradyan Arttırma Regresyonu bu çalışmada kullanılmıştır. Modelin performansını ölçmek adına gerçek HVAC verileri kullanılarak anomali tespit yöntemleriyle karşılaştırılmıştır. Sonuçlar, yanlış alarmları en aza indirirken sistemin anormallikleri doğru bir şekilde tanımlama becerisinde önemli gelişmeler olduğunu göstermektedir. Genel olarak, bu araştırma daha sağlam ve uyarlanabilir bir anomali tespit çözümü sağlayarak HVAC sistem güvenliğinin ilerlemesine katkıda bulunmaktadır. Bu çalışma, Gradyan Arttırma Regresyonu'nun HVAC sistemlerinin siber güvenlik çerçevesine entegrasyonu ile siber tehditlere karşı gelişmiş koruma sağlayacağı ve böylece kritik altyapıların esnekliğini ve güvenilirliğini arttıracağını göstermiştir.

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