TY - JOUR
T1 - Robust WLAN-based indoor fine-grained intrusion detection
AU - Lv, Jiguang
AU - Yang, Wu
AU - Gong, Liangyi
AU - Man, Dapeng
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Intrusion detection plays a critical role in security of people's possessions. Approaches such as video-based, infrared-based, RFID, UWB, etc. can provide satisfying detection accuracy. However, they all require specialized hardware deployment and strict using conditions which hinder their wide deployment. Beyond communication, WLANs can also act as generalized sensor networks and there are several researches working on motion detection via WLAN due to its advantages in deployment flexibility, coverage, and cost efficiency. Nevertheless, they are unsuitable for intrusion detection as none of them can accurately detect human motion when the moving speed is very slow. This paper proposes SIED as an accurate method for Speed Independent device-free Entity Detection which is suitable for intrusion detection even when the entity's moving speed is very slow. The influence becomes much smaller when the entity is moving with a very slow speed. Previous methods have the limitations in that their performance downgrades sharply when the entity's moving speed is very slow. Recently, it has been shown that Channel State Information (CSI) at PHY layer of wireless network has the potential to detect moving entities more accurately. In this paper we leverage CSI of 802.11n wireless network and probability technique to detect entities of different moving speeds. SIED captures the variance of variances of amplitudes of each CSI subcarrier, and combines Hidden Markov Model (HMM) to make entity detection a probability problem. We implement SIED using commercial WiFi devices and evaluate our method using two typical testbeds and show that SIED can achieve an average detection accuracy of greater than 98% under different entity moving speed.
AB - Intrusion detection plays a critical role in security of people's possessions. Approaches such as video-based, infrared-based, RFID, UWB, etc. can provide satisfying detection accuracy. However, they all require specialized hardware deployment and strict using conditions which hinder their wide deployment. Beyond communication, WLANs can also act as generalized sensor networks and there are several researches working on motion detection via WLAN due to its advantages in deployment flexibility, coverage, and cost efficiency. Nevertheless, they are unsuitable for intrusion detection as none of them can accurately detect human motion when the moving speed is very slow. This paper proposes SIED as an accurate method for Speed Independent device-free Entity Detection which is suitable for intrusion detection even when the entity's moving speed is very slow. The influence becomes much smaller when the entity is moving with a very slow speed. Previous methods have the limitations in that their performance downgrades sharply when the entity's moving speed is very slow. Recently, it has been shown that Channel State Information (CSI) at PHY layer of wireless network has the potential to detect moving entities more accurately. In this paper we leverage CSI of 802.11n wireless network and probability technique to detect entities of different moving speeds. SIED captures the variance of variances of amplitudes of each CSI subcarrier, and combines Hidden Markov Model (HMM) to make entity detection a probability problem. We implement SIED using commercial WiFi devices and evaluate our method using two typical testbeds and show that SIED can achieve an average detection accuracy of greater than 98% under different entity moving speed.
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U2 - 10.1109/GLOCOM.2016.7842238
DO - 10.1109/GLOCOM.2016.7842238
M3 - Conference article
AN - SCOPUS:85015457371
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 7842238
T2 - 59th IEEE Global Communications Conference, GLOBECOM 2016
Y2 - 4 December 2016 through 8 December 2016
ER -