Robust WLAN-Based Indoor Fine-Grained Intrusion Detection

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

doi:10.1109/glocom.2016.7842238

Cited by 38 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 2: By solving the convex quadratic programming problem to solve w and b in Eq. (17), the predictive function is obtained, and the objective function is minimized:…”

Section: Online Behavior Testing Phasementioning

confidence: 99%

See 1 more Smart Citation

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

View full text Add to dashboard Cite

Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

show abstract

“…Step 2: By solving the convex quadratic programming problem to solve w and b in Eq. (17), the predictive function is obtained, and the objective function is minimized:…”

Section: Online Behavior Testing Phasementioning

confidence: 99%

“…Wang et al used a method of deterministic CSI fingerprinting and threshold-based [17]. In order to detect a human in an omnidirectional way, Wu et al proposed DeMan [8] for device-free detection of moving and stationary human.…”

Section: Introductionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…When people pass through the room, they used CSI instead of RSSI and took into account the frequency-selective fading characteristics of indoor wireless channels while eliminating consumption impact of power level fluctuations in wireless hardware. Lv et al [24] proposed a detection method that will not be affected by the moving speed of the target. They captured the variance of the amplitude differences of the CSI subcarriers and the HMM (Hidden Markov model) was used to solve the problem of entity detection.…”

Section: Related Workmentioning

confidence: 99%

CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

et al. 2018

View full text Add to dashboard Cite

Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection). Through this method, fine-grained physical layer Channel State Information (CSI) can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target.

show abstract

“…Besides being used for communication, WiFi networks can also be used as sensor networks [2][3][4]. Many applications have emerged based on WiFi infrastructures, human detection [5], indoor localization [6], and even human identification [7] are some representative applications.…”

Section: Introductionmentioning

confidence: 99%

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Man

Yang

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.

show abstract

Robust WLAN-Based Indoor Fine-Grained Intrusion Detection

Cited by 38 publications

References 14 publications

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

Contact Info

Product

Resources

About