EXPERIMENTAL TERMINAL FOR EVALUATION OF Wi-Fi SPATIAL IDENTIFICATION METHODS IN CRITICAL INFORMATION INFRASTRUCTURE AND ANALYSIS OF THE PRACTICAL EFFECT OF IMPLEMENTATION

Authors

DOI:

https://doi.org/10.31673/2409-7292.2026.023312

Abstract

The article substantiates the concept and architecture of an experimental polygon for reproducible and safe
evaluation of spatial identification methods for Wi-Fi sources in critical information infrastructure (CII) environments.
The requirements for the polygon are formulated: controlled sensor topology, zones with shielding and intentional multipath, dynamic interference, as well as the possibility of safe implementation of typical attacks (rogue AP, evil twin,
deauthentication) outside productive contours. A system of metrics is proposed that integrates localization quality (mean
and median error, 95th percentile), detection and classification indicators (Precision, Recall, F1, ROC-AUC) and SOC
operational characteristics (mean time to detection, time to source localization, false positive rate, analyst working time).
The data collection protocol and rules for forming training and test subsets taking into account the drift of the radio
environment are described. Separately, a model for assessing the practical effect of implementation is presented, which
links technical metrics with the risk of incidents, downtime of services and response costs. It is experimentally shown
that hybrid localization methods provide a median error of 1.12 m versus 2.7 m for classical RSSI trilateration, and the
hybrid attack detector achieves ROC-AUC of 0.962. The results are suitable for testing, implementation in the CII and
preparation of materials according to the requirements of professional publications.
Keywords: Wi-Fi cybersecurity; critical information infrastructure; spatial localization; experimental polygon;
rogue AP detection; SOC; performance metrics.

References
1. Frankel S., Eydt B., Owens L., Scarfone K. Establishing Wireless Robust Security Networks: A Guide to IEEE
802.11i. NIST Special Publication 800-97. Gaithersburg, MD: National Institute of Standards and Technology, 2007. 162
p. DOI: https://doi.org/10.6028/NIST.SP.800-97.
2. Souppaya M., Scarfone K. Guidelines for Securing Wireless Local Area Networks (WLANs). NIST Special
Publication 800-153. Gaithersburg, MD: National Institute of Standards and Technology, 2012. 46 p. DOI:
https://doi.org/10.6028/NIST.SP.800-153.
3. Scarfone K. A., Dicoi D., Sexton M., Tibbs C. Guide to Securing Legacy IEEE 802.11 Wireless Networks.
NIST Special Publication 800-48 Rev. 1. Gaithersburg, MD : National Institute of Standards and Technology, 2008. DOI:
https://doi.org/10.6028/NIST.SP.800-48r1.
4. Liu H., Darabi H., Banerjee P., Liu J. Survey of wireless indoor positioning techniques and systems // IEEE
Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 2007. Vol. 37, no. 6. P. 1067-1080.
DOI: https://doi.org/10.1109/TSMCC.2007.905750.
5. Zafari F., Gkelias A., Leung K. K. A Survey of Indoor Localization Systems and Technologies // IEEE
Communications Surveys and Tutorials. 2019. Vol. 21, no. 3. P. 2568-2599. DOI: https://doi.org/10.1109/ COMST.
2019.2911558.
6. Xu Q., Zheng R., Saad W., Han Z. Device Fingerprinting in Wireless Networks: Challenges and Opportunities
// IEEE Communications Surveys and Tutorials. 2016. Vol. 18, no. 1. P. 94-104. DOI: https://doi.org/10.1109/
COMST.2015.2476338.
7. Soltanieh N., Norouzi Y., Yang Y., Karmakar N. C. A Review of Radio Frequency Fingerprinting Techniques
// IEEE Journal of Radio Frequency Identification. 2020. Vol. 4, no. 3. P. 222-233. DOI: https://doi.org/10.1109/
JRFID.2020.2968369.
8. Restuccia F., D’Oro S., Melodia T. Securing the Internet of Things in the Age of Machine Learning and
Software-Defined Networking // IEEE Internet of Things Journal. 2018. DOI: https://doi.org/10.1109/JIOT.
2018.2846040.
9. Vielberth M., Böhm F., Fichtinger I., Pernul G. Security Operations Center: A Systematic Study and Open
Challenges // IEEE Access. 2020. Vol. 8. P. 227756-227779. DOI: https://doi.org/10.1109/ACCESS.2020.3045514.
10. Adesina D., Hsieh C.-C., Sagduyu Y. E., Qian L. Adversarial Machine Learning in Wireless Communications
Using RF Data: A Review // IEEE Communications Surveys and Tutorials. 2023. Vol. 25, no. 1. P. 77-100. DOI:
https://doi.org/10.1109/COMST.2022.3205184.
11. Wong L. J., Michaels A. J. Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and
Survey // Sensors. 2022. Vol. 22, no. 4. Art. 1416. DOI: https://doi.org/10.3390/s22041416.
12. Shang S., Wang L. Overview of WiFi fingerprinting-based indoor positioning // IET Communications. 2022.
Vol. 16, no. 7. P. 725-733. DOI: https://doi.org/10.1049/cmu2.12386.
13. Singh N., Choe S., Punmiya R. Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints:
An Overview // IEEE Access. 2021. Vol. 9. P. 127150-127174. DOI: https://doi.org/10.1109/ACCESS.2021.3111083.
14. Meng W., Xiao W., Ni W., Xie L. Secure and robust Wi-Fi fingerprinting indoor localization // 2011
International Conference on Indoor Positioning and Indoor Navigation. 2011. P. 1-7. DOI: https://doi.org/ 10.1109/
IPIN.2011.6071908.
15. He S., Lin W., Chan S.-H. G. Indoor Localization and Automatic Fingerprint Update with Altered AP Signals
// IEEE Transactions on Mobile Computing. 2017. Vol. 16, no. 7. DOI: https://doi.org/10.1109/TMC.2016.2608946.
16. Dai S., He L., Zhang X. Autonomous WiFi Fingerprinting for Indoor Localization // 2020 ACM/IEEE 11th
International Conference on Cyber-Physical Systems (ICCPS). 2020. P. 141-150. DOI: https://doi.org/10.1109/
ICCPS48487.2020.00021.
17. Subbu K. P., Gozick B., Dantu R. Indoor localization through dynamic time warping // Proceedings of the
IEEE International Conference on Systems, Man and Cybernetics. 2011. P. 1639-1644. DOI: https://doi.org/10.1109/
ICSMC.2011.6083906.
18. Ledlie J., Park J.-G., Curtis D., Cavalcante A. M., Camara L., Costa A., Vieira R. D. Molé: A scalable, usergenerated WiFi positioning engine // Journal of Location Based Services. 2012. Vol. 6, no. 2. DOI: https://doi.org/
10.1080/17489725.2012.692617.
19. Wang F., Feng J., Zhao Y., Zhang X., Zhang S., Han J. Joint Activity Recognition and Indoor Localization
With WiFi Fingerprints // IEEE Access. 2019. Vol. 7. P. 80058-80068. DOI: https://doi.org/10.1109/ ACCESS.2019.
2923743.
20. Zhang Z., Lee M., Choi S. Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of
Trajectories // Sensors. 2021. Vol. 21, no. 17. Art. 5776. DOI: https://doi.org/10.3390/s21175776.
21. Feng X., Nguyen K. A., Luo Z. WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the
Signal Round Trip Time // Remote Sensing. 2022. Vol. 14, no. 23. Art. 6052. DOI: https://doi.org/10.3390/rs14236052.
22. Chang R. Y., Liu S.-J., Cheng Y.-K. Device-Free Indoor Localization Using Wi-Fi Channel State Information
for Internet of Things // 2018 IEEE Global Communications Conference (GLOBECOM). 2018. P. 1-7. DOI:
https://doi.org/10.1109/GLOCOM.2018.8647261.
23. Bassey J., Adesina D., Li X., Qian L., Aved A., Kroecker T. Intrusion Detection for IoT Devices based on RF
Fingerprinting using Deep Learning // 2019 Fourth International Conference on Fog and Mobile Edge Computing
(FMEC). 2019. P. 98-104. DOI: https://doi.org/10.1109/FMEC.2019.8795319.
24. Song X., Fan X., He X., Xiang S., Chen K. CNNLoc: Deep-Learning Based Indoor Localization with WiFi
Fingerprinting // 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing,
Scalable Computing and Communications, Internet of People and Smart City Innovation. 2019. P. 589-595. DOI:
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139.
25. Jiang H., Peng C., Sun J. Deep Belief Network for Fingerprinting-Based RFID Indoor Localization // ICC
2019 - 2019 IEEE International Conference on Communications. 2019. DOI: https://doi.org/10.1109/ICC.2019.8761800.

Published

2026-06-25

Issue

Section

Articles