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. 2025 Dec 18;25(24):7680.
doi: 10.3390/s25247680.

Cybersecure Intelligent Sensor Framework for Smart Buildings: AI-Based Intrusion Detection and Resilience Against IoT Attacks

Md Abubokor Siam  1 , Khadeza Yesmin Lucky  1 , Syed Nazmul Hasan  2 , Jobanpreet Kaur  2 , Harleen Kaur  2 , Md Salah Uddin  2 , Mia Md Tofayel Gonee Manik  1
Affiliations

Cybersecure Intelligent Sensor Framework for Smart Buildings: AI-Based Intrusion Detection and Resilience Against IoT Attacks

Md Abubokor Siam et al. Sensors (Basel). .

Abstract

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security.

Keywords: Artificial Intelligence (AI); Internet of Things (IoT); Intrusion Detection Systems (IDS); cybersecurity; smart buildings.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
IoT-based smart building architecture with AI-based IDS.
Figure 2
Figure 2
Methodology flow diagram.
Figure 3
Figure 3
The workflow for the proposed AI-based Intrusion Detection System in smart building environments.
See this image and copyright information in PMC

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