Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM

It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates 100%: (i) authorized ID verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB, and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

[1]  Sheldon A. Munns,et al.  RF-DNA Fingerprinting for Airport WiMax Communications Security , 2010, 2010 Fourth International Conference on Network and System Security.

[2]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

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

[4]  Ziya Telatar,et al.  RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum , 2019, IEEE Access.

[5]  Michael A. Temple,et al.  Detecting rogue attacks on commercial wireless Insteon home automation systems , 2018, Comput. Secur..

[6]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[8]  John F. Doherty,et al.  Nonlinearity estimation for Specific Emitter Identification in multipath environment , 2009, 2009 IEEE Sarnoff Symposium.

[9]  Kevin W. Sowerby,et al.  Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers , 2014, J. Comput. Syst. Sci..

[10]  T. Charles Clancy,et al.  Security in Cognitive Radio Networks: Threats and Mitigation , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[11]  Hossein Jafari,et al.  IoT Devices Fingerprinting Using Deep Learning , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).

[12]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[13]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[14]  Daisuke Takahashi,et al.  IEEE 802.11 user fingerprinting and its applications for intrusion detection , 2010, Comput. Math. Appl..

[15]  Donald R. Reising,et al.  Exploitation of RF-DNA for device classification and verification using GRLVQI processing , 2012 .

[16]  T. Daniel Loveless,et al.  RF-DNA Fingerprint Classification of OFDM Signals Using a Rayleigh Fading Channel Model , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[17]  Anthony N. Mucciardi,et al.  A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties , 1971, IEEE Transactions on Computers.

[18]  R.P.L. Durgabai,et al.  Feature Selection using ReliefF Algorithm , 2014 .

[19]  Michael A. Temple,et al.  Authorized and Rogue Device Discrimination Using Dimensionally Reduced RF-DNA Fingerprints , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[21]  Benjamin W. P. Ramsey,et al.  An RF-DNA verification process for ZigBee networks , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[22]  John F. Doherty,et al.  Nonlinearity Estimation for Specific Emitter Identification in Multipath Channels , 2011, IEEE Transactions on Information Forensics and Security.

[23]  Robert F. Mills,et al.  Radio frequency fingerprinting commercial communication devices to enhance electronic security , 2008, Int. J. Electron. Secur. Digit. Forensics.

[24]  Aboul Ella Hassanien,et al.  Linear discriminant analysis: A detailed tutorial , 2017, AI Commun..

[25]  N. Serinken,et al.  Characteristics of radio transmitter fingerprints , 2001 .

[26]  Ryan M. Gerdes,et al.  Crowdsourced measurements for device fingerprinting , 2019, WiSec.

[27]  Marc Geilen,et al.  On the discrete Gabor transform and the discrete Zak transform , 1996, Signal Process..

[28]  Michael A. Temple,et al.  Improved wireless security for GMSK-based devices using RF fingerprinting , 2010, Int. J. Electron. Secur. Digit. Forensics.

[29]  James R. Ottewill,et al.  Relief F-Based Feature Ranking and Feature Selection for Monitoring Induction Motors , 2018, 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR).

[30]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[31]  Chunyi Peng,et al.  How Can IoT Services Pose New Security Threats In Operational Cellular Networks , 2020 .

[32]  Barnard Kroon,et al.  Steady State RF Fingerprinting for Identity Verification: One Class Classifier versus Customized Ensemble , 2009, AICS.

[33]  Michael A. Temple,et al.  Sensitivity Analysis of Burst Detection and RF Fingerprinting Classification Performance , 2009, 2009 IEEE International Conference on Communications.

[34]  O. H. Tekbas,et al.  Improvement of transmitter identification system for low SNR transients , 2004 .

[35]  D. Toher,et al.  Why Welch’s test is Type I error robust , 2016 .

[36]  Witold Kinsner,et al.  Transient analysis and genetic algorithms for classification , 1995, IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings.

[37]  Anthony Skjellum,et al.  A Hardware-Software Codesign Approach to Identity, Trust, and Resilience for IoT/CPS at Scale , 2019, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[38]  Jose A. Gonzalez,et al.  Numerical Analysis for Relevant Features in Intrusion Detection (NARFid) , 2009 .

[39]  Charles G. Wheeler,et al.  Assessment of the impact of CFO on RF-DNA fingerprint classification performance , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[40]  O. Ureten,et al.  Detection of radio transmitter turn-on transients , 1999 .

[41]  Baldini Gianmarco,et al.  Physical Layer authentication and identification of wireless devices using the Synchrosqueezing transform , 2018 .

[42]  Yu Bai,et al.  Specific Emitter Identification Techniques for the Internet of Things , 2020, IEEE Access.

[43]  T. Daniel Loveless,et al.  Integration of Matched Filtering within the RF-DNA Fingerprinting Process , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[44]  Shahid Mumtaz,et al.  New Security Mechanisms of High-Reliability IoT Communication Based on Radio Frequency Fingerprint , 2019, IEEE Internet of Things Journal.

[45]  Indrajit Ray,et al.  Device Identity and Trust in IoT-sphere Forsaking Cryptography , 2019, 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC).

[46]  Samir S. Soliman,et al.  Signal classification using statistical moments , 1992, IEEE Trans. Commun..

[47]  Jin Wang,et al.  An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology , 2020, Sensors.

[48]  Walid Saad,et al.  Device Fingerprinting in Wireless Networks: Challenges and Opportunities , 2015, IEEE Communications Surveys & Tutorials.

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

[50]  G. Ruxton.  The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test , 2006 .

[51]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[52]  Wei Yang,et al.  Neighborhood Component Feature Selection for High-Dimensional Data , 2012, J. Comput..

[53]  J. Dudczyk,et al.  Applying the radiated emission to the specific emitter identification , 2004, 15th International Conference on Microwaves, Radar and Wireless Communications (IEEE Cat. No.04EX824).

[54]  Hua Peng,et al.  Specific Emitter Identification Based on Deep Residual Networks , 2019, IEEE Access.

[55]  Muazzam Ali Khan,et al.  Automatic Modulation Recognition of Communication Signals. , 2012 .

[56]  Michael A. Temple,et al.  Improving ZigBee Device Network Authentication Using Ensemble Decision Tree Classifiers With Radio Frequency Distinct Native Attribute Fingerprinting , 2015, IEEE Transactions on Reliability.

[57]  Lajos Hanzo,et al.  Physical-layer authentication for wireless security enhancement: current challenges and future developments , 2016, IEEE Communications Magazine.

[58]  Srinivasan Seshan,et al.  802.11 user fingerprinting , 2007, MobiCom '07.

[59]  Srdjan Capkun,et al.  Transient-based identification of wireless sensor nodes , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[60]  Alexandr M. Kuzminskiy,et al.  RF Fingerprint detection in a wireless multipath channel , 2010, 2010 7th International Symposium on Wireless Communication Systems.

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