Improving the security of wireless communication channels for unmanned aerial vehicles by creating false information fields

Improving the security of wireless communication channels for unmanned aerial vehicles by creating false information fields

Автор: Basan E.S., Proshkin N.A., Silin O.I.

Журнал: Siberian Aerospace Journal @vestnik-sibsau-en

Рубрика: Aviation and spacecraft engineering

Статья в выпуске: 4 vol.23, 2022 года.

Бесплатный доступ

To date, the problems associated with the safety of unmanned aerial vehicles (UAVs) are quite acute. As a rule, when it comes to commercial small-sized UAVs, wireless communication channels are used to con-trol them. Most often, communication is implemented at a frequency of 2.4 GHz using the Wi-Fi protocol. Such a UAV is quite easy to detect by analyzing the radio frequency range or the data link layer. An attack-er, however, may not even have specialized equipment and use open source software. The detected UAV becomes the target for attacks. If it is known that the UAV operates as a wireless access point, then all Wi-Fi-specific attacks become relevant for the UAV. In this study, it is proposed to use the technology of creat-ing false information fields as the first line of defense to increase the resistance of the UAV to attacks. This technology will allow to hide a legitimate UAV communication channel behind a lot of fake ones. The goal is to create fake access points with the characteristics of real ones and emulate data transmission over the channels on which these access points are deployed. In addition to the fact that the technology allows to hide a legitimate UAV communication channel, it will also allow to mislead the attacker. It is important to make the intruder think that not a single UAV is approaching him, but a group. If the intruder attempts to attack decoys, attacker will compromise himself and be able to be detected. Thus, you can use the UAV as a bait. As a result of the pilot study, channels were identified on which the creation of fake access points is most effective. Using small computing power and the necessary antenna, you can achieve high results. This article demonstrates the effectiveness of creating 9 fake access points. A comparison was also made with real wireless network traffic. We can say that the emulated activity is quite close to the real activity.

Еще

Wireless communication channels, access point, radio intelligence, security, vulnerabilities

Короткий адрес: https://sciup.org/148329659

IDR: 148329659   |   DOI: 10.31772/2712-8970-2022-23-4-657-670

Список литературы Improving the security of wireless communication channels for unmanned aerial vehicles by creating false information fields

  • Xu C., Liao X., Tan J., Ye H., Lu H. Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude. IEEE Access. 2020, Vol. 8. P. 74175–74194. Doi: 10.1109/ACCESS.2020.2987622.
  • Shi Y., Bai M., Li Y. Study on UAV Remote Sensing Technology in Irrigation District Infor-mationization Construction and Application. 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). Changsha, China, 2018, P. 252–255. Doi: 10.1109/ICMTMA.2018.00067.
  • Gao X., Jia H., Chen Z., Yuan G., Yang S. UAV security situation awareness method based on semantic analysis. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). Shenyang, China, 2020, P. 272–276. Doi: 10.1109/ICPICS50287.2020.9201954.
  • Basan E., Medvedev M., Teterevyatnikov S. Analysis of the Impact of Denial-of-Service At-tacks on the Group of Robots. International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), China, 2018, P. 63–68. Doi: 10.1109/CyberC.2018.00023.
  • Bernardini A., Mangiatordi F., Pallotti E., Capodiferro L. Drone detection by acoustic signature identification. IS TInt. Symp. Electron. Imaging Sci. Technol. 2017, P. 60–64.
  • Kim J., Park C., Ahn J., Ko Y., Park J., Gallagher J. C. Real-time UAV sound detection and analysis system. IEEE Sensor Application Symposium (SAS). United States, 2017, P. 1–5.
  • Nijim M., Mantrawadi N. Drone classification and identification system by phenome analysis using data mining techniques. IEEE Symposium on Technologies for Homeland Security. Waltham, MA, United States, 2016, P. 1–5.
  • Aker C., Kalkan S. Using deep networks for drone detection. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Lecce, Italy, 2017, P. 1–6. Doi: 10.1109/AVSS.2017.8078539.
  • Saqib M., Daud Khan S., Sharma N., Blumenstein M. A study on detecting drones using deep convolutional neural networks. 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Lecce, Italy, 2017, P. 1–5. Doi: 10.1109/AVSS.2017.8078541.
  • Nguyen P., Ravindranathan M., Nguyen A., Han R., Vu T. Investigating cost-effective RF-based detection of drones. 2nd Workshop on Micro Aerial Vehicle Networks, Systems, and Applica-tions for Civilian Use (DroNet '16). Association for Computing Machinery. New York, NY, USA, 2016, P. 17–22. Doi: https://doi.org/10.1145/2935620.2935632.
  • Ezuma M., Erden F., Anjinappa C. K., Ozdemir O., Guvenc I. Micro-UAV Detection and Clas-sification from RF Fingerprints Using Machine Learning Techniques. IEEE Aerospace Conference. Big Sky, MT, USA, 2019, P. 1–13. Doi: 10.1109/AERO.2019.8741970.
  • Abeywickrama S., Jayasinghe L., Fu H., Nissanka S., Yuen C. RF-based Direction Finding of UAVs Using DNN. IEEE International Conference on Communication Systems (ICCS). Chengdu, China, 2019, P. 157–161. Doi: 10.1109/ICCS.2018.8689177.
  • Fonseca R., Creixell W. Tracking and following a moving object with a quadcopter. 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Lecce, Italy, 2017, P. 1–6. Doi: 10.1109/AVSS.2017.8078463.
  • Kim B. K., Kang H. S., Park S. O. Drone classification using convolutional neural networks with merged doppler images. IEEE Geoscience and Remote Sensing Letters. 2017, Vol. 14, No. 1, P. 38–42.
  • Davaslioglu K., Sagduyu Y. E. Generative adversarial learning for spectrum sensing. IEEE In-ternational Conference on Communications (ICC). Kansas City, MO, USA, 2018, P. 1–6. Doi: 10.1109/ICC.2018.8422223.
  • He H., Wen C.-K., Jin S., Li G. Y. A model-driven deep learning network for MIMO detection. IEEE Transactions on Signal Processing. 2018, Vol. 68, P. 1702–1715.
  • Ye H., Li G. Y., Juang B.-H. Power of deep learning for channel estimation and signal detec-tion in OFDM systems. IEEE Wireless Communications Letters. 2018, Vol. 7, No. 1, P. 114–117. Doi: 10.1109/LWC.2017.2757490.
  • O’Shea T. J., Hoydis J. An introduction to deep learning for the physical layer // IEEE Transac-tions on Cognitive Communications and Networking (TCCN). 2017. Vol. 3, No. 4. P. 563–575. Doi: 10.1109/TCCN.2017.2758370.
  • Shi Y., Sagduyu Y. E., Erpek T., Davaslioglu K., Lu Z., Li J. Adversarial deep learning for cognitive radio security: jamming attack and defense strategies. IEEE ICC 2018 Workshop – Promises and Challenges of Machine Learning in Comm. Networks. 2018, P. 1–6. Doi: 10.1109/ICCW.2018.8403655.
  • Shi Y., Erpek T., Sagduyu Y. E., Li J. Spectrum data poisoning with adversarial deep learning. IEEE Military Communications Conference. Los Angeles, CA, USA, 2018, P. 407–412. Doi: 10.1109/MILCOM.2018.8599832.
  • Sagduyu Y. E., Shi Y., Erpek T. IoT network security from the perspective of adversarial deep learning. IEEE International Conference on Sensing, Communication and Networking (SECON) Workshop on Machine Learning for Communication and Networking in IoT. Boston, MA, USA, 2019, P. 1–9. Doi: 10.1109/SAHCN.2019.8824956.
  • Erpek T., Sagduyu Y. E., Shi Y. Deep learning for launching and mitigating wireless jamming attacks. IEEE Transactions on Cognitive Communications and Networking. 2019, Vol. 5, No. 1, P. 2–14. Doi: 10.1109/TCCN.2018.2884910.
  • Shi Y., Davaslioglu K., Sagduyu Y. E. Generative adversarial network for wireless signal spoofing. ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) Work-shop on Wireless Security and Machine Learning (WiseML). Miami FL USA, 2019, P. 55–60. Doi: https://doi.org/10.1145/3324921.3329695.
  • Abu Zainab N. et al. QoS and jamming-aware wireless networking using deep reinforcement learning. IEEE Military Communications Conference (MILCOM). Norfolk, VA, USA, 2019, P. 610–615. Doi: 10.1109/MILCOM47813.2019.9020985.
  • Restuccia F. et al. DeepRadioID: Real-time channel-resilient optimization of deep learning-based radio fingerprinting algorithms. ACM Intl. Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). New York, NY, United States, 2019, P. 51–60. Doi: https://doi.org/10.1145/3323679.3326503.
  • Soltani S. Distributed cognitive radio network architecture, SDR implementation and emulation testbed. IEEE Military Communications Conference (MILCOM). Tampa, FL, USA, 2015, P. 438–443. Doi: 10.1109/MILCOM.2015.7357482.
  • Sagduyu Y. E., Berry R., Ephremides A. Jamming games in wireless networks with incomplete information. IEEE Communications Magazine. 2011, Vol. 49, No. 8, P. 112–118. Doi: 10.1109/MCOM.2011.5978424.
  • O’Shea T., Corgan J., Clancy C. Convolutional radio modulation recognition networks // Communications in Computer and Information Science. 2016, Vol. 629. Springer, Cham. Doi: https://doi.org/10.1007/978-3-319-44188-7_16.
  • O’Shea T. J., Roy T., Clancy T. C. Over-the-air deep learning-based radio signal classification. IEEE Journal of Selected Topics in Signal Processing. 2018, Vol. 12, No. 1, P. 168–179. Doi: 10.1109/JSTSP.2018.2797022.
  • Ali A., Fan Y. Unsupervised feature learning and automatic modulation classification using deep learning model. Physical Communication. 2017, Vol. 25, P. 75–84.
  • Shi Y. et al. Deep learning for signal classification in unknown and dynamic spectrum envi-ronments. IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). New-ark, NJ, USA, 2019, P. 1–10. Doi: 10.1109/DySPAN.2019.8935684.
  • Kiranyaz S., Zabihi M., Rad A. B., Tahir A., Ince T., Hamila R. Real-time PCG Anomaly De-tection by Adaptive 1D Convolutional Neural Networks. Signal Processing. 2016, P. 1–12. Doi: https://doi.org/10.48550/arXiv.1902.07238.
  • Zheng Y., Liu Q., Chen E., Ge Y., Zhao J. L. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front. Comput. Sci. 2016, Vol. 10, No. 1, P. 96–112.
  • Mikhalevich I. F., Trapeznikov V. A. Critical Infrastructure Security: Alignment of Views. In Systems of Signals Generating and Processing in the Field of on Board Communications. Moscow Technical University of Сommunications and Informatics, Russia, 2019, P. 1–5. Doi: 10.1109/SOSG.2019.8706821.
  • Ilioudis C. V., Clemente C., Soraghan J. Understanding the potential of Self-Protection Jam-ming on board of miniature UAVs. In International Radar Conference (RADAR). Toulon, France, 2019, P. 1–6. Doi: 10.1109/RADAR41533.2019.171405.
  • Li X., Ju R., Wang H., Sun Y. The Design and Research of Data Transmission on Remote Ra-dio Control in Different Noise Channel. 13th World Congress on Intelligent Control and Automation (WCICA). Changsha, China, 2018, P. 1306–1311. Doi: 10.1109/WCICA.2018.8630340.
  • Proshkin N., Basan E., Lapina M. Radio Frequency Method for Emulating Multiple UAVs. 17th International Conference on Intelligent Environments (IE). Dubai, United Arab Republic, 2021, P. 1–4. Doi: 10.1109/IE51775.2021.9486599.
  • Basan E., Basan A., Nekrasov A., Fidge C., Sushkin N., Peskova O. GPS-Spoofing Attack De-tection Technology for UAVs Based on Kullback–Leibler Divergence. Drones. 2022, No. 6(1), P. 8. Doi: https://doi.org/10.3390/drones6010008.
  • Astaburuaga I., Lombardi A., La Torre B., Hughes C., Sengupta S. Vulnerability Analysis of AR. Drone 2.0, an Embedded Linux System. IEEE 9th Annual Computing and Communication Work-shop and Conference (CCWC). United States, 2019, P. 0666–0672. Doi: 10.1109/CCWC. 2019.8666464.
  • Caballé M. C., Augé A. C., Lopez-Aguilera E., Garcia-Villegas E., Demirkol I., Aspas J. P. An Alternative to IEEE 802.11ba: Wake-Up Radio with Legacy IEEE 802.11 Transmitters. IEEE Access. 2019, Vol. 7, P. 48068–48086. Doi: 10.1109/ACCESS.2019.2909847.
  • Madruga S., Tavares A., Brito A., Nascimento T. A Project of an Embedded Control System for Autonomous Quadrotor UAVs. Latin American Robotic Symposium, Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). João Pessoa, Brazil, 2018, P. 483–489. Doi: 10.1109/LARS/SBR/WRE.2018.00091.
  • Carranza A., Mayorga D., DeCusatis C. Comparison of Wireless Network Penetration Testing Tools on Desktops and Raspberry Pi Platforms. 16th LACCEI International Multi-Conference for En-gineering, Education and Technology. Boca Raton, Florida, USA, 2018, P. 1–5.
  • Abril-García J. H. et al. Application to monitoring a USB control with Python in Windows, Mac OS and Raspbian OS. ECORFAN Journal Democratic Republic of Congo. 2019, Vol. 5 (8), P. 1–6.
  • Korneev S. Digital spectrum analyzer GSP-7830 manufactured by GW Instek. Components and technologies. 2008, Vol. 1 (78), P. 158–162.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp