Robust WLAN-based indoor fine-grained intrusion detection

Jiguang Lv, Wu Yang, Liangyi Gong, Dapeng Man, Xiaojiang Du

Research output: Contribution to journalConference articlepeer-review

38 Scopus citations

Abstract

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.

Original languageEnglish
Article number7842238
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2016
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: 4 Dec 20168 Dec 2016

Fingerprint

Dive into the research topics of 'Robust WLAN-based indoor fine-grained intrusion detection'. Together they form a unique fingerprint.

Cite this