Yazılım Tanımlı Radyoya Dayalı RF Parmak İzi Toplamak için Düşük Maliyetli Bir Çözüm

Yıl 2022, Cilt: 2 Sayı: 2, 179 - 188, 31.12.2022

Hüseyin Parmaksız , Cihan Karakuzu

Öz

Teknoloji ve sistemlerin gelişmesiyle nesnelerin internet (IoT) kullanımı her geçen gün artmaktadır. IoT cihazlarının yaygın kullanımı, bu sistemlerin nasıl güvence altına alınabileceği sorusunu gündeme getirmektedir. Kaynak kısıtlamaları nedeniyle IoT cihazlarında güvenlik önlemi almak mümkün değildir. Bu nedenle, IoT cihazlarında güvenlik giderek daha önemli hale geliyor. Literatür incelendiğinde kablosuz cihazlar için ek bir güvenlik katmanı olarak radyo frekansı parmak izi teknikleri kullanıldığı görülmektedir. Kimlik sahtekarlığı veya kimlik sahtekarlığı saldırılarını önlemek için güvenlik amacıyla kablosuz cihazları tanımlamak için cihazların donanım bileşenlerindeki üretim kusurları nedeniyle benzersiz parmak izleri kullanılmaktadır. Bu çalışmada, IoT güvenliğini artırmak için etkili bir yöntem olarak kullanılan kablosuz sinyallerin toplama sistemi gerçeklenmiştir.

Anahtar Kelimeler

DragonOS, Gnu radio, Hackrf one, IoT, radio frekans parmak izi

Destekleyen Kurum

Bilecik Şeyh Edebali Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Proje Numarası

2021-01.BŞEÜ.01-01

Teşekkür

Tüm akademik çalışmalarımda olduğu gibi bu yayında da önderliği, yardım ve desteklerini esirgemeyen doktora tez danışmanım Prof. Dr. Cihan KARAKUZU’ya teşekkür ederim.

A Low Cost Solution for Software Defined Radio Based RF Fingerprint Collection

Öz

The use of the Internet of Things (IoT) is increasing with the advancement of technology and systems. The widespread use of IoT devices begs the question of how to secure these systems. Due to resource constraints, it is not possible to implement security measures on IoT devices. As a result, security is becoming increasingly important in IoT devices. Radio frequency fingerprinting techniques are used as an additional security layer for wireless devices, according to the literature. Unique fingerprints are used to identify wireless devices for security purposes to prevent spoofing or spoofing attacks due to manufacturing defects in the hardware components of the devices. In this study, a wireless signal acquisition system is implemented as an effective method of increasing IoT security.

Anahtar Kelimeler

DragonOS, Gnu radio, Hackrf one, IoT, radio frequency fingerprint, software defined radio.

Proje Numarası

2021-01.BŞEÜ.01-01

Kaynakça

• Abirami, M., Hariharan, V., Sruthi, M., Gandhiraj, R., & Soman, K. (2013). Exploiting GNU radio and USRP: An economical test bed for real time communication systems. 2013 fourth international conference on computing, communications and networking technologies (ICCCNT).
• Akhtyamov, R., Golkar, A., & Hanson, M. (2015). Development and stratospheric flight demonstration of a SDR-enabled Federated System. Jun-2015.[Online]. Available: https://www. researchgate. net/profile/Rustam_Akhtyamov/publication/282279134_Development_and_stratospheric_flight_demonstration_o f_a_SDR-enabled_Federated_System/links/560a4bdb08ae1396914bb27c. pdf.[Accessed: 14-Jan-2020].
• Al-Shawabka, A., Restuccia, F., D’Oro, S., & Melodia, T. (2020). Massive-Scale I/Q Datasets for WiFi Radio Fingerprinting. Computer Networks, 182, 107566.
• Barbeau, M., Hall, J., & Kranakis, E. (2006). Detection of rogue devices in bluetooth networks using radio frequency fingerprinting. proceedings of the 3rd IASTED International Conference on Communications and Computer Networks, CCN.
• Chen, L., Zhao, C., Zheng, Y., & Wang, Y. (2021). Radio Frequency Fingerprint Identification Based on Transfer Learning. 2021 IEEE/CIC International Conference on Communications in China (ICCC),
• Ezuma, M., Erden, F., Anjinappa, C. K., Ozdemir, O., & Guvenc, I. (2019). Micro-UAV detection and classification from RF fingerprints using machine learning techniques. 2019 IEEE Aerospace Conference,
• Gummineni, M., & Polipalli, T. R. (2020). Implementation of reconfigurable transceiver using GNU Radio and HackRF One. Wireless Personal Communications, 112(2), 889-905.
• Huang, D., Al-Hourani, A., Sithamparanathan, K., Rowe, W. S., Bulot, L., & Thompson, A. (2021). Deep Learning Methods for Device Authentication Using RF Fingerprinting. 2021 15th International Conference on Signal Processing and Communication Systems (ICSPCS),
• Köse, M., Taşcioğlu, S., & Telatar, Z. (2019). RF fingerprinting of IoT devices based on transient energy spectrum. IEEE Access, 7, 18715-18726.
• Lin, T.-Y., Lai, C.-M., & Chen, C.-W. (2020). Using SDR Platform to Extract the RF Fingerprint of the Wireless Devices for Device Identification. CS & IT Conference Proceedings,
• Liu, Y., Wang, J., Niu, S., & Song, H. (2021). ADS-B signals records for non-cryptographic identification and incremental learning. IEEE, Piscataway, NJ, USA, Data Set.
• Liu, Y., Wang, J., Song, H., Niu, S., & Thomas, Y. (2020). A 24-hour signal recording dataset with labels for cybersecurity and IoT. IEEE, Piscataway, NJ, USA, Data Set.
• Mohanti, S., Soltani, N., Sankhe, K., Jaisinghani, D., Di Felice, M., & Chowdhury, K. (2020). AirID: Injecting a custom RF fingerprint for enhanced UAV identification using deep learning. GLOBECOM 2020-2020 IEEE Global Communications Conference,
• Nouichi, D., Abdelsalam, M., Nasir, Q., & Abbas, S. (2019). IoT devices security using RF fingerprinting. 2019 Advances in Science and Engineering Technology International Conferences (ASET),
• Parmaksız, H., & Karakuzu, C. (2022). A Review of Recent Developments on Secure Authentication Using RF Fingerprints Techniques. Sakarya University Journal of Computer and Information Sciences, 5(3),
• Perotoni, M. B., & dos Santos, K. M. (2021). SDR-based spectrum analyzer based in open-source GNU radio. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 20, 542-555.
• Reus-Muns, G., Jaisinghani, D., Sankhe, K., & Chowdhury, K. R. (2020). Trust in 5G open RANs through machine learning: RF fingerprinting on the POWDER PAWR platform. GLOBECOM 2020-2020 IEEE Global Communications Conference,
• Stewart, R. W., Barlee, K. W., Atkinson, D. S., & Crockett, L. H. (2015). Software defined radio using MATLAB & Simulink and the RTL-SDR.
• Ureten, O., & Serinken, N. (2007). Wireless security through RF fingerprinting. Canadian Journal of Electrical and Computer Engineering, 32(1), 27-33.
• Uzundurukan, E., Dalveren, Y., & Kara, A. (2020). A database for the radio frequency fingerprinting of Bluetooth devices. Data, 5(2), 55.
• Valkanas, A., Pandey, D., & Leib, H. (2019). Surfing the radio spectrum using RTL-SDR. IETE Journal of Education, 60(2), 65-73.
• VonEhr, K., Neuson, W., & Dunne, B. E. (2016). Software defined radio: choosing the right system for your communications course. 2016 ASEE annual conference & exposition.
• Xu, C., Chen, B., Liu, Y., He, F., & Song, H. (2020). RF fingerprint measurement for detecting multiple amateur drones based on STFT and feature reduction. 2020 Integrated Communications Navigation and Surveillance Conference (ICNS).
• Yu, J., Hu, A., Zhou, F., Xing, Y., Yu, Y., Li, G., & Peng, L. (2019). Radio frequency fingerprint identification based on denoising autoencoders. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).