Evidence map›Paper›PMID 41471675›Full record

ArticleSensors (Basel, Switzerland)2025

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

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

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Md Abubokor SiamCollege of Business, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0001-5250-4652
Khadeza Yesmin LuckyCollege of Business, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0002-6186-485X
Syed Nazmul HasanCollege of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0008-0977-595X
Jobanpreet KaurCollege of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0008-0083-8205
Harleen KaurCollege of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0009-7062-0700
Md Salah UddinCollege of Technology & Engineering, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0001-0741-1393
Mia Md Tofayel Gonee ManikCollege of Business, Westcliff University, Irvine, CA 92614, USA.ORCID 0009-0005-6098-5213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial Intelligence (AI)cybersecurityInternet of Things (IoT)Intrusion Detection Systems (IDS)smart buildings

Identifiers

PMID41471675
PMCPMC12736962

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.