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.
Simultaneous Intrusion Detection and Localization Using ISAC Network / Shakoor, Usama; Bilal Janjua, Muhammad; Solaija, Muhammad Sohaib J.; Arslan, Huseyin. - (2025). (Intervento presentato al convegno 2025 8th International Balkan Conference on Communications and Networking (Balkancom) tenutosi a Piraeus, Greece) [10.1109/Balkancom65827.2025.11186044].
Simultaneous Intrusion Detection and Localization Using ISAC Network
Usama Shakoor
Primo
Conceptualization
;
2025
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 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


