Simultaneous Intrusion Detection and Localization Using ISAC Network

Fuente: arXiv
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Autores principales: Shakoor, Usama, Janjua, Muhammad Bilal, Solaija, Muhammad Sohaib J., Arslan, Huseyin
Formato: Preprint
Publicado: 2025
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author Shakoor, Usama
Janjua, Muhammad Bilal
Solaija, Muhammad Sohaib J.
Arslan, Huseyin
author_facet Shakoor, Usama
Janjua, Muhammad Bilal
Solaija, Muhammad Sohaib J.
Arslan, Huseyin
contents 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 proposed 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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id arxiv_https___arxiv_org_abs_2505_07656
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Simultaneous Intrusion Detection and Localization Using ISAC Network
Shakoor, Usama
Janjua, Muhammad Bilal
Solaija, Muhammad Sohaib J.
Arslan, Huseyin
Signal Processing
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 proposed 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.
title Simultaneous Intrusion Detection and Localization Using ISAC Network
topic Signal Processing
url https://arxiv.org/abs/2505.07656