Robust Wireless Fingerprinting via Complex-Valued Neural Networks

A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wireless signals resides in complex baseband, in this paper, we explore the use of neural networks with complex- valued weights to learn fingerprints using supervised learning. We demonstrate that, while there are potential benefits to using sections of the signal beyond just the preamble to learn fingerprints, the network cheats when it can, using information such as transmitter ID (which can be easily spoofed) to artificially inflate performance. We also show that noise augmentation by inserting additional white Gaussian noise can lead to significant performance gains, which indicates that this counter-intuitive strategy helps in learning more robust fingerprints. We provide results for two different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness of the proposed method.

[1]  Jakob Hoydis,et al.  An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.

[2]  Raheem Beyah,et al.  GTID: A Technique for Physical Device and Device Type Fingerprinting , 2015, IEEE Transactions on Dependable and Secure Computing.

[3]  T. Charles Clancy,et al.  Convolutional Radio Modulation Recognition Networks , 2016, EANN.

[4]  Chrisil Arackaparambil,et al.  On the reliability of wireless fingerprinting using clock skews , 2010, WiSec '10.

[5]  Ivan Martinovic,et al.  On Passive Data Link Layer Fingerprinting of Aircraft Transponders , 2015, CPS-SPC '15.

[6]  Marco Gruteser,et al.  Wireless device identification with radiometric signatures , 2008, MobiCom '08.

[7]  Jingyu Hua,et al.  Accurate and Efficient Wireless Device Fingerprinting Using Channel State Information , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[8]  Keith E. Nolan,et al.  Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[9]  T. Kohno,et al.  Remote physical device fingerprinting , 2005, 2005 IEEE Symposium on Security and Privacy (S&P'05).

[10]  Shauna Revay,et al.  Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks , 2018, IEEE Journal of Selected Topics in Signal Processing.

[11]  Mauro Leonardi,et al.  Air Traffic Security: Aircraft Classification Using ADS-B Message’s Phase-Pattern , 2017 .

[12]  Akira Hirose,et al.  Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Stratis Ioannidis,et al.  ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks , 2018, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[14]  Sandeep Subramanian,et al.  Deep Complex Networks , 2017, ICLR.

[15]  Sneha Kumar Kasera,et al.  On Fast and Accurate Detection of Unauthorized Wireless Access Points Using Clock Skews , 2010, IEEE Transactions on Mobile Computing.