Simultaneous Intrusion Detection and Localization Using ISAC Network

The rapid increase in utilization of smart home technologies has introduced new paradigms to ensure the security and privacy of inhabitants. In this study, we propose a novel approach to detect and localize physical intrusions in indoor environments. The proposed method leverages signals from access points (APs) and an anchor node (AN) to achieve accurate intrusion detection and localization. We evaluate its performance through simulations under different intruder scenarios. The pro-posed method achieved a high accuracy of 92 % for both intrusion detection and localization. Our simulations demonstrated a low false positive rate of less than 5 % and a false negative rate of around 3 %, highlighting the reliability of our approach in identifying security threats while minimizing unnecessary alerts. This performance underscores the effectiveness of integrating Wi-Fi sensing with advanced signal processing techniques for enhanced smart home security.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/balkancom65827.2025.11186044
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
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article

Simultaneous Intrusion Detection and Localization Using ISAC Network

Hüseyin Arslan, Muhammad Bilal Janjua, Muhammad Sohaib J. Solaija, Usama Shakoor
Indoor and Outdoor Localization Technologies
article

Simultaneous Intrusion Detection and Localization Using ISAC Network

Hüseyin Arslan, Muhammad Bilal Janjua, Muhammad Sohaib J. Solaija, Usama Shakoor
article en

Abstract

The rapid increase in utilization of smart home technologies has introduced new paradigms to ensure the security and privacy of inhabitants. In this study, we propose a novel approach to detect and localize physical intrusions in indoor environments. The proposed method leverages signals from access points (APs) and an anchor node (AN) to achieve accurate intrusion detection and localization. We evaluate its performance through simulations under different intruder scenarios. The pro-posed method achieved a high accuracy of 92 % for both intrusion detection and localization. Our simulations demonstrated a low false positive rate of less than 5 % and a false negative rate of around 3 %, highlighting the reliability of our approach in identifying security threats while minimizing unnecessary alerts. This performance underscores the effectiveness of integrating Wi-Fi sensing with advanced signal processing techniques for enhanced smart home security.

Gebze Technical University (TR), Istanbul Medipol University (TR), Turkish Society of Hematology (TR)
Openalex Percentile: Top 58%
Indoor and Outdoor Localization Technologies
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