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


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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Access Points


Cumulative Density Function


Channel State Information


Detection Accuracy


False Negative


False Positive


Generative Adversarial Network


Gaussian Process Regression


Hidden Markov Model


K-Nearest Neighbor


Location-based Services




Moving Average


Maximum Mean Discrepancy


Mobile Points


Moving Variance


Network Interface Controller


Probabilistic Neural Network


Pattern Recognition Neural Network


Random Forest


Radio Frequency Identification


Reproducing Kernel Hilbert Space


Received Signal Strength


Support Vector Machine


Universal Software Radio Peripheral


Wireless Local Area Network


  1. 1.

    Ullah F (2020) Intelligence and security in big 5G-oriented IoNT: an overview. Futur Gener Comput Syst 102(1):357–368.

    Article  Google Scholar 

  2. 2.

    Alabady SA, Fadi AT, Sadia D (2018) A novel security model for cooperative virtual networks in the IoT era. Int J Parallel Prog 48:280–295.

    Article  Google Scholar 

  3. 3.

    Al-turjman, F (2019) Smart-cities medium access for smart mobility applications in IoT, Transactions on Emerging Telecommunications Technologies Article number: e3723. 10.1002/ett.3723

  4. 4.

    Yang Z, Zhou Z, Liu Y (2013) From RSSI to CSI: indoor localization via channel response. ACM Comput Surv 46(2):1–32

    Article  Google Scholar 

  5. 5.

    Poongodi M, Vijayakumar V, Al-Turjman F, Hamdi M, Ma M (2019) Intrusion prevention system for DDoS attack on VANET with reCAPTCHA controller using information based metrics. IEEE Access 7:158481–158491.

    Article  Google Scholar 

  6. 6.

    Guan L, Hu F, Al-Turjman F, Khan MB, Yang X (2019) A non-contact paraparesis detection technique based on 1D-CNN. IEEE Access 7:182280–182288.

    Article  Google Scholar 

  7. 7.

    Ullah F, Naeem H, Jabber S, Khalid S, Latif MA, Al-turjman F (2019) Cyber security threats detection in internet of things using deep learning approach. IEEE Access 7:124379–124389.

    Article  Google Scholar 

  8. 8.

    Al-urjman F, Zahmatkesh H, Shahroze R (2019) Article number: e3677) An overview of security and privacy in smart cities IoT communications. Trans Emerg Telecommun Technol.

  9. 9.

    Chowdhury SA, Uddin MN, Kowsar MMS, Deb K (2016) Occlusion handling and human detection based on histogram of oriented gradients for automatic video surveillance. In: Proceedings of the IEEE international conference on innovations in science, Eng Technol:1–4.

  10. 10.

    Sahoo KC, Pati UC (2017) IoT based intrusion detection system using PIR sensor, in: proceedings of IEEE international conference on recent trends in electronics. Inform Commun Technol:1641–1645.

  11. 11.

    Li Y, Song Y, Zhao Y, Zhao S, Li X, Li L, Tang S (2017) An infrared target detection algorithm based on lateral inhibition and singular value decom-position. Infrared Phys Technol 85:238–245.

    Article  Google Scholar 

  12. 12.

    Youssef M, Mah M, Agrawala A (2007) Challenges: Device-free passive localization for wireless environments, in: Proc ACM Int Confer Mobile Comput Netw 222–229.

  13. 13.

    Wang J, Tian Z, Yang X, Zhou M (2019) CSI component reconstruction-based AoA estimation for subtle human-induced reflection under the TTW scenario. IEEE Commun Lett 33(8):1393–1396.

    Article  Google Scholar 

  14. 14.

    Wang J, Tian Z, Yang X, Zhou M (2019) TWPalo: through-the-wall passive localization of moving human with Wi-fi. IEEE Global Commun Conference:1–6.

  15. 15.

    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359.

    Article  Google Scholar 

  16. 16.

    Liu K, Zhang H, Ng JKY, Xia Y, Feng L, Lee VCS, Son SH (2018) Toward low-overhead fingerprint-based indoor localization via transfer learning: design, implementation, and evaluation. IEEE Trans Ind Informatics 14(3):898–908.

    Article  Google Scholar 

  17. 17.

    Pan C, Huang J, Gong J, Yuan X (2019) Few-shot transfer learning for text classification with lightweight word embedding based models. IEEE Access 7:53296–53304.

    Article  Google Scholar 

  18. 18.

    Zhou R, Chen J, Lu X, Wu J (2017) CSI fingerprinting with SVM regression to achieve device-free passive localization, in: proceedings of the IEEE international symposium on a world of wireless. Mobile Multimed Netw:1–9.

  19. 19.

    A. E. Kosba, A. Saeed, M. Youssef (2012) RASID: A robust WLAN device-free passive motion detection system. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications 180–189.

  20. 20.

    Deak G, Curran K, Condell J, Deak D (2014) Detection of multi-occupancy using device-free passive localization. IET Wireless Sens Syst 4(3):130–137.

    Article  Google Scholar 

  21. 21.

    Tian Z, Zhou X, Zhou M, Li S, Shao S (2015) Indoor device-free passive localization for intrusion detection using multi-feature PNN, in: Proceedings of the International Conference on Communications and Networking in China. 272–277.

  22. 22.

    Lv J, Man D, Yang W, Du X, Yu M (2018) Robust WLAN-based indoor intrusion detection using PHY layer information. IEEE Access 6:30117–30127.

    Article  Google Scholar 

  23. 23.

    Tan Q, Han C, Sun L, Guo J, Zhu H (2018) A CSI frequency domain fingerprint-based method for passive indoor human detection, in: proceedings of the IEEE international conference on trust. Secur Privacy Comput Commun:1832–1837.

  24. 24.

    Gu Y, Zhan J, Ji Y, Li J, Ren F, Gao S (2017) MoSense: an RF-based motion detection system via off-the-shelf WiFi devices. IEEE Internet Things J 4(6):2326–2341.

    Article  Google Scholar 

  25. 25.

    Moussa M, Youssef M (2009) Smart cevices for smart environments: Device-free passive detection in real environments. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications 1–6.

  26. 26.

    J. Lv, W. Yang, L. Gong, D. Man, X. Du Robust WLAN-based Indoor Fine-grained Intrusion Detection. In: Proceedings of IEEE ICC

  27. 27.

    Jun J, He L, Gu Y, Jiang W, Kushwaha G, Vipin A, Cheng L, Liu C, Zhu T (2018) Low-overhead wifi fingerprinting. IEEE Trans Mobile Comput 17(3):590–603.

    Article  Google Scholar 

  28. 28.

    Shu Y, Huang Y, Zhang J, Cou P, Cheng P, Chen J, Shin KG (2015) Gradient-based fingerprinting for indoor localization and tracking. IEEE Trans Ind Electron 63(4):2424–2433.

    Article  Google Scholar 

  29. 29.

    Eleryan A, Elsabagh M, Youssef M (2011) Synthetic generation of radio maps for device-free passive localization. In: Proceedings of IEEE Global Telecom- munications Conference. 1–5.

  30. 30.

    Gunawan M, Li B, Gallagher T, Dempster AG., Retscher G (2012) A new method to generate and maintain a wifi fingerprinting database automatically by using rfid. In: Proceedings of International Conference on Indoor Positioning and Indoor Navigation. 1–6.

  31. 31.

    Lemic F, Handziski V, Caso G, Nardis LD., Wolisz A (2016) Enriched training database for improving the wifi rssi-based indoor fingerprinting performance. In: Proceedings of IEEE Annual Consumer Communications Networking Conference. 875–881.

  32. 32.

    He C, Guo S, Wu Y, Yang Y (2016) A novel radio map construction method to reduce collection effort for indoor localization. Measurement 94(08):423–431.

    Article  Google Scholar 

  33. 33.

    Cho Y, Kim J, Ji M, Lee Y, Park S (2013) Gpr based wi-fi radio map construction from real/virtual indoor dynamic surveying data, in: proceedings of international conference on control, Automation Syst 712–714.

  34. 34.

    Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. Adv Neural Inf Proces Syst 3(6):2672–2680

    Google Scholar 

  35. 35.

    Li Q, Qu H, Liu Z 2018 AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System,

  36. 36.

    Qian Q, Jin R, Yi J, Zhang L, Zhu S (2015) Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD). Mach Learn 99(3):353–372.

    MathSciNet  Article  MATH  Google Scholar 

  37. 37.

    Gretton A, Sriperumbudur B, Sejdinovic D, Strathmann H, Balakrishnan S, Pontil M, Fukumizu K (2012) Optimal kernel choice for large-scale two-sample tests. In: Proceedings of the International Conference on Neural Information Processing Systems. 1025–1213

  38. 38.

    Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210.

    Article  Google Scholar 

  39. 39.

    Scholkopf B, Smola A, Muller K-R (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319.

    Article  Google Scholar 

  40. 40.

    Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27.

    Article  MATH  Google Scholar 

  41. 41.

    Breiman L (2001) Random forests Mach Learn 45(1):5–32.

    Article  Google Scholar 

  42. 42.

    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297.

    Article  MATH  Google Scholar 

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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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Correspondence to Mu Zhou.

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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).

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  • Personnel intrusion detection
  • Database extension
  • Generative adversarial network
  • Transfer learning
  • Wireless local area network