DOI: https://doi.org/10.15587/1729-4061.2019.157001

Improving the model of decision making about abnormal network state using a positioning system

Ivan Antipov, Tetyana Vasilenko

Abstract


We have proposed to supplement the model of decision making about abnormal states of a wireless network under conditions of uncertainty by another attribute ‒ the location of wireless mobile and stationary devices in a controlled network.

The method of trilateration, based on the measurement of signal strength at three points, is considered. This method has a high accuracy of determining the location of a wireless device, provided that the most accurate model of radio waves propagation is constructed. However, given the specificity of radio waves propagation inside the premise, it is rather difficult to build such a model for them. Therefore, it is proposed to use the method of radio fingerprints. This method is based on the construction of radio maps for each of the three access points, which indicates the signal level from a typical wireless device located at a certain number of reference points. We have also considered the possibility of the combined application of two methods, which will make it possible to determine the location of a wireless device even when it is outside the radio map.

Experimental studies were carried out, including the creation of radio maps for a room of area 70 m² with 26 reference points. We employed three identical routers and a smartphone. During the experiment, it turned out that, depending on the orientation of the mobile device (in fact, its antenna), the measured power changes, so the radio maps were constructed based on average power for six different positions of the mobile device. It is shown that the level of the signal is almost independent of the door and window position in the room.

This analysis of the principles of organizing various types of attacks on wireless networks has revealed that accounting for the position makes it possible to detect attacks of the types "man in the middle" and "false access point" that were not identified by the base model. In addition, the improved model allows determining the source of interference at the "muting" attack

Keywords


signal strength; trilateration; radio fingerprints method; radio map; location of wireless subscriber

References


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Bargshady, N., Garza, G., Pahlavan, K. (2016). Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter. IEEE Sensors Journal, 16 (24), 8963–8971. doi: https://doi.org/10.1109/jsen.2016.2616758

Atia, M. M., Noureldin, A., Korenberg, M. J. (2012). Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks. IEEE Wireless Communications Letters, 1 (2), 101–104. doi: https://doi.org/10.1109/wcl.2012.020612.110279

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GOST Style Citations


Vsesvitnie doslidzhennia ekonomichnykh zlochyniv ta shakhraistva 2018 roku: rezultaty opytuvannia ukrainskykh orhanizatsiyi // PwC. URL: https://www.pwc.com/ua/uk/survey/2018/pwc-gecs-2018-ukr.pdf

Kotov V. D., Vasil'ev V. I. Current state of network intrusion detection // Vestnik Ufimskogo gosudarstvennogo aviacionnogo tekhnicheskogo universiteta. 2012. Vol. 16, Issue 3 (48). P. 198–204.

Los' A. B., Danielyan Yu. Yu. Sravnitel'nyy analiz sistem obnaruzheniya vtorzheniy, predstavlennyh na otechestvennom rynke // Vestnik Moskovskogo finansovo-yuridicheskogo universiteta. 2014. Issue 3. P. 181–187.

Antipov I. E., Yashchenko T. A., Nasif N. T. Primenenie nechetkoy logiki dlya povysheniya bezopasnosti besprovodnyh setey na baze tekhnologii Wi-Fi // Radiotekhnika. 2011. Issue 165. P. 103–106.

Markin D. O. Issledovanie effektivnosti algoritmov opredeleniya mestopolozheniya mobil'nyh ustroystv vnutri pomeshcheniya // Vestnik RGRTU. 2015. Issue 54. P. 32–39.

The Cisco Hyperlocation Module: Best of Interop Awards Finalist // Cisco Blogs. URL: https://blogs.cisco.com/wireless/the-cisco-hyperlocation-module-best-of-interop-awards-finalist

Yurkin D. V., Nikitin V. N. Intrusion detection systems in IEEE 802.11 local wireless networks // Informacionno-upravlyayushchie sistemy. 2014. Issue 2. P. 44–49.

Niculescu D., Nath B. Ad hoc positioning system (APS) using AOA // IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428). 2003. doi: https://doi.org/10.1109/infcom.2003.1209196 

Teoreticheskie osnovy radiolokacii / Shirman Ya. D., Golikov V. N., Busygin I. N., Kostin G. A., Manzhos V. N., Minervin N. N. et. al. Moscow: Sovetskoe radio, 1970. 560 p.

PinPoint: An asynchronous time-based location determination system / Youssef M., Youssef A., Rieger C., Shankar U., Agrawala A. // Proceedings of the 4th international conference on Mobile systems, applications and services – MobiSys 2006. 2006. P. 165–176. doi: https://doi.org/10.1145/1134680.1134698 

Cong L., Zhuang W. Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems // IEEE Transactions on Wireless Communications. 2002. Vol. 1, Issue 3. P. 439–447. doi: https://doi.org/10.1109/twc.2002.800542 

Bargshady N., Garza G., Pahlavan K. Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter // IEEE Sensors Journal. 2016. Vol. 16, Issue 24. P. 8963–8971. doi: https://doi.org/10.1109/jsen.2016.2616758 

Atia M. M., Noureldin A., Korenberg M. J. Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks // IEEE Wireless Communications Letters. 2012. Vol. 1, Issue 2. P. 101–104. doi: https://doi.org/10.1109/wcl.2012.020612.110279 

ITU-R P.1238-9 – Propagation data and prediction methods for the planning of indoor radio communication systems and the radio local area networks in the frequency range 300 MHz to 100 GHz. Geneva: ITU-R Recommendations, 2017.

Zymbler M. L., Miniakhmetov R. M., Rogov A. A. The survey of indoor positioning algorithms for mobile devices // Bulletin of the South Ural State University. Series "Computational Mathematics and Software Engineering". 2013. Vol. 2, Issue 2. P. 83–96. doi: https://doi.org/10.14529/cmse130207 







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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061