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Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded Database

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Abstract

The Internet of Everything (IoE) provides a platform that allows devices to be remotely connected, sensed, and controlled across the network infrastructure. The smart home in the era of the IoE is born on the basis of the high integration of emerging communication technologies such as big data, sensors, and machine learning. In this paper, we focus on wireless detection technologies using smartphones and computers in smart homes. Among them, the indoor Wireless Local Area Network (WLAN) personnel intrusion detection technology based on the database construction has become one of the comprehensive detection technologies by advantages of the convenient accessibility of the WLAN signal and minimal hardware requirement. However, the considerable labor and time cost involved in the database construction affects the popularity and application of database-based intrusion detection systems. To cope with this problem, we propose a new indoor WLAN personnel intrusion detection approach with the reduced overhead of the database construction. Specifically, first of all, the offline database is extended by fake Received Signal Strength (RSS) data, which are generated by the Generative Adversarial Network (GAN) based supervised learning from actual labeled RSS data. Second, the difference between the extended database and online RSS data caused by the time-variant environment noise is reduced by minimizing the Maximum Mean Discrepancy (MMD) between marginal distributions of RSS data through the transfer learning. Finally, the intrusion detection is achieved by classifying online RSS data with classifiers trained from the extended database. Furthermore, experimental results show that the proposed approach can not only perform well in reducing the database overhead and the difference of data in source and target domains, which are corresponding to the same environment state but also detect environment states with satisfactory accuracy.

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Abbreviations

APs:

Access Points

CDF:

Cumulative Density Function

CSI:

Channel State Information

DA:

Detection Accuracy

FN:

False Negative

FP:

False Positive

GAN:

Generative Adversarial Network

GPR:

Gaussian Process Regression

HMM:

Hidden Markov Model

KNN:

K-Nearest Neighbor

LBSs:

Location-based Services

LOS:

Line-of-sight

MA:

Moving Average

MMD:

Maximum Mean Discrepancy

MPs:

Mobile Points

MV:

Moving Variance

NIC:

Network Interface Controller

PNN:

Probabilistic Neural Network

PRNN:

Pattern Recognition Neural Network

RF:

Random Forest

RFID:

Radio Frequency Identification

RKHS:

Reproducing Kernel Hilbert Space

RSS:

Received Signal Strength

SVM:

Support Vector Machine

USRP:

Universal Software Radio Peripheral

WLAN:

Wireless Local Area Network

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61771083, 61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), and Postgraduate Scientific Research and Innovation Project of Chongqing (CYS18240).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Chongqing, 400065, Nan’an District, China

    Mu Zhou, Yaoping Li, Hui Yuan & Jiacheng Wang

  2. Hong Kong Baptist University, Hong Kong, 999077, China

    Qiaolin Pu

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Zhou, M., Li, Y., Yuan, H. et al. Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded Database. Mobile Netw Appl 26, 1024–1042 (2021). https://doi.org/10.1007/s11036-020-01663-8

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