Hybrid Machine Learning for IoT-Enabled Smart Buildings

• DOI: 10.3390/informatics12010017
• Publication Date: 2025-02-11T10:34:32Z
• Authors: Robert-Alexandru ...; Simona Iuliana Ca...; Ștefan Mocanu; Radu Nicolae Piet...; Mihnea Alexandru ...
• Keywords: Building Automation

This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices edge, specifically for those utilizing TCP/IP protocol. Recognizing critical challenges posed by rapid expansion networks, this work evaluates proposed IDS model with primary focus on optimizing training time without sacrificing accuracy. The begins comprehensive review existing models IDS, highlighting both their strengths and limitations. It then provides overview technologies methodologies implemented in work, including utilization “Botnet Traffic Dataset For Smart Buildings”, newly released public dataset tailored threat detection. is explained detail, followed discussion experimental results that assess model’s performance real-world conditions. Furthermore, evaluated its effectiveness within smart building environments, demonstrating how it can address unique such as resource constraints real-time edge. aims to contribute development efficient, reliable, scalable solutions protect ecosystems from emerging threats.