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international conference on computing networking and communications communications and information security symposium gabor based rf dna fingerprinting for classifying 802 16e wimax mobile subscribers donald r reising michael a temple ...

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                   International Conference on Computing, Networking and Communications, Communications and Information Security Symposium
                           Gabor-Based RF-DNA Fingerprinting for
                 Classifying 802.16e WiMAX Mobile Subscribers
                                                Donald R. Reising, Michael A. Temple and Mark E. Oxley
                                                           US Air Force Institute of Technology
                                                          Wright-Patterson AFB, OH 45433 USA
                                                              Email: michael.temple@afit.edu
                Abstract—Previous work has demonstrated the viability of           The motivation for considering Worldwide Interoperability
             using RF-DNA fingerprinting to provide serial number discrim-        for Microwave Access (WiMAX) signals is provided by 1) the
             ination of IEEE 802.11a WiFi devices as a means to augment          continued proliferation of IEEE 802.16e last mile communica-
             conventional bit-level security. This was done using RF-DNA         tions, to include Long Term Evolution (LTE), and 2) potential
             extracted from signal regions containing standard pre-defined        adoption of an IEEE 802.16e compliant solution for next
             responses (preamble, midamble, etc.). Using these responses,
             proof-of-concept demonstrations with RF-DNA fingerprinting           generation airport communication services as being pursued
             have shown some effectiveness for providing serial number dis-      by the FAA, Eurocontrol and International Civilian Aviation
             crimination. The discrimination challenge increases considerably    Organization (ICAO) [8], [9]. WiMAX architectures are based
             whenpre-defined signal responses are not present. This challenge     on Wireless Access Points (WAP) that are among the top
             is addressed here using experimentally collected IEEE 802.16e       10 IT threats [10] and unauthorized access to public safety
             WiMAX signals from Alvarion BreezeMAX Mobile Subscriber
             (MS) devices. Relative to previous Time Domain (TD) and             services is a major concern [9]. The goal here is to develop
             Spectral Domain (SD) fingerprint features, joint time-frequency      RF-DNAtechniquesthataregenerally adaptable to the class of
             Gabor (GT) and Gabor-Wigner (GWT) Transform features are            Orthogonal Frequency Division Multiplexed (OFDM) signals
             considered here as a means to extract greater device discriminat-   versus being limited to a given system implementation.
             ing information. For comparison, RF-DNA is extracted from TD,         The methodology here is consistent with [4]–[7], [11]
             SD, GT, and GWT responses and MDA/ML feature extraction
             and classification performed. Preliminary assessment shows that      which extracted RF-DNA from near-transient (e.g., 802.11a
             Gabor-based RF-DNA fingerprinting is much more effective than        preamble) and non-transient (e.g., GSM midamble) signal
             either TD or SD methods. GT RF-DNA fingerprinting achieves           regions for RF-DNA fingerprinting. These regions contain
             individual WiMAX MS device classification of 98.5% or better         “pre-defined” standard responses that are commonly used for
             for SNR ≥Š3 dB.                                                     device synchronization, channel estimation, network timing,
                                   I. INTRODUCTION                               etc. Ideally, these standard responses would be identical for
                Work continues on exploiting Physical (PHY) layer at-            all devices and would not include the unintentional coloration
             tributes of the Open System Interconnection (OSI) model to          that produces RF-DNA uniqueness. The work reported here
             augment bit-level security. The goal is to reduce or eliminate      progresses toward signal regions that do not contain pre-
             unauthorized network access while assuring access for autho-        defined responses.
             rized users [1]–[7]. The authors here are pursuing a better           Theneedtoconsidersignal regions void of pre-defined stan-
             understanding of inherent PHY layer benefits provided through        dard responses was driven by empirical assessment using an
             Radio Frequency “Distinct Native Attribute” (RF-DNA) Fin-           Alvarion BreezeMAX Extreme 5000 system. As implemented,
             gerprinting. Specifically, work in [4]–[7] has successfully used     the Downlink (DL) Base Transceiver Station (BTS) signal
             RF-DNAfromselectedportions of modulated signal responses            includes a distinct preamble response while the Uplink (UL)
             to achieve serial number discrimination.                            Mobile Subscriber (MS) signals do not. Thus, the challenge
                Exploitable RF-DNA attributes are 1) “native” from the           with classifying BreezeMAX BTS devices is consistent with
             time of manufacture and vary according to hardware im-              results reported in [12]. However, the challenge is greatly
             plementation, component type, manufacturing processes, etc.,        increased when considering the MS signals which do not
             and 2) sufficiently “distinct” to enable reliable cross-device       contain a preamble or midamble response (consistent with the
             discrimination. Ideally, RF-DNA is only a function of unin-         802.16e standard [13], [14]). Common options for increasing
             tended “coloration” in modulated signal responses and when          classification performance include 1) finding a feature space
             extracted, it can be processed to provide reliable device           that provides greater discrimination using a given classifier,
             discrimination. Such processing generally involves feature se-      2) finding a more powerful classification engine for a given
             lection, model generation, and device classification. To enable      feature space, or 3) some combination thereof. As a first step,
             qualitative comparative assessment of improvements gained by        the authors here are considering an alternate feature space
             introducing Gabor RF-DNA features, work here is based on            involving joint Time-Frequency (T-F) signal responses.
             Multiple Discriminant Analysis (MDA) feature selection with           Benefits for using joint T-F features with RF-DNA finger-
             Maximum Likelihood (ML) classification [4]–[7].                      printing were demonstrated in [4], [5] using a Dual-Tree Com-
                   U.S. Government work not protected by U.S. copyright       7
                 plex Wavelet Transform (DT-CWT). In these works, statistical                                     10
                 RF-DNA was extracted from complex DT-CWT responses of                                          )  8
                 OFDM-based 802.11a preambles. The T-F resolution trade-                                        V
                                                                                                                m
                                                                                                                (  6
                 off of the DT-CWT (increasing time resolution decreases                                        e
                                                                                                                d
                                                                                                                u
                 frequency resolution and visa-versa) is potentially limiting.                                  it 4
                 Given this T-F trade-off is not present with the Gabor (GT)                                    agn
                 and Gabor-Wigner (GWT) Transforms, this work investigates                                      M  2
                 their use as an alternate feature space for RF-DNA fingerprint                                     00      0.2     0.4     0.6    0.8      1      1.2    1.4     1.6
                 generation. While both the GT and GWT transforms provide                                                                      Time (ms)
                 T-F localization, the GWT combines the advantages of the GT                                               (a) MS Data Only Sub-Frame Response.
                 (lack of cross-terms) with that of the Wigner-Ville Distribution
                 (higher clarity/resolution) [15].
                    The remainder of this paper is arranged as follows: Sect. II                                ) 8
                 provides an overview of the Alvarion BreezeMAX system                                          V
                                                                                                                m
                                                                                                                ( 6
                 used for demonstration; Sect. III provides the Demonstration                                   e
                                                                                                                d
                                                                                                                u
                 Methodology, to include a description of Signal Collection and                                 it4
                 Detection, RF-DNA Fingerprinting, and MDA/ML Process-                                          agn
                 ing. Device Classification Results are presented in Sect. IV                                    M 2
                 followed by a Conclusion in Sect.V.                                                              00     0.2    0.4   0.6    0.8    1     1.2    1.4   1.6    1.8
                                       IMAXDEMONSTRATIONSYSTEM                                                                                 Time (ms)
                              II. W
                    Alvarion BreezeMax Extreme 5000 equipment was used                                                  (b) MS Range-Plus-Data Sub-Frame Response.
                 for demonstration [16]. The system uses 60/40 Time Division                                      10
                 Duplexing (TDD) with the first 60% of each TF =5mSec                                            )  8
                 TDDframe allotted for BTS DL transmission and the remain-                                      V
                                                                                                                m
                                                                                                                (
                 ing 40% allotted for MS UL transmission. An RF channel                                         e  6
                                                                                                                d
                                                                                                                u
                 bandwidth of W             =5MHz centered at f = 5475 MHz                                      it
                                        ch                                    c                                    4
                 was used for demonstration. Figure 1 shows experimentally                                      agn
                 observed UL sub-frame responses (preceding DL sub-frame                                        M  2
                 responses not shown), designated herein as Data Only, Range-                                      00     0.2   0.4    0.6   0.8     1    1.2    1.4   1.6    1.8
                 Plus-Data,andRange Only responses–designations that were                                                                      Time (ms)
                 neither confirmed with Alvarion nor readily apparent in doc-                                               (c) MS Range Only Sub-Frame Response.
                 umentation. All subsequent discussion and results in Sect. IV
                 are based on MS Ranging Only operation.                                                Fig. 1.   Magnitude plots for three distinct UL sub-frame responses observed
                    Unlike previous GSM and 802.11 signals that have been                               during experimental collection of BreezeMAX 802.16e WiMAX Mobile
                 considered [4]–[7], [11], the BreezeMAX Extreme MS signals                             Subscriber (MS) signals–designated herein as “modes” to facilitate discussion.
                 lack a distinct region where identical modulation occurs across
                 all devices. However, as most observable in Fig. 1(c) and
                 expanded upon in Fig. 2, all responses contain a constant bias                         12 bit analog-to-digital converter operating at fs =95mega-
                 that spans the UL sub-frame. This was observed for all devices                         samples-per-second (Msps). The IF signal is down-converted,
                 tested and believed to be incorporated by design to stabilize                          digitally filtered using a WBB =9.28 MHz filter, sub-
                 electronic component response and mitigate adverse peak-to-                            sampled (Nyquist satisfied), and stored as complex In-phase
                 average power ratio effects that commonly occur with OFDM.                             (I) and Quadrature (Q) data. The WiMAX MS devices and
                 The “near-transient” MS response in Fig. 2 ( ≈ 2.0 to 16.0
                 µSec) has thus far yielded the most useful RF-DNA and is
                 used exclusively for all results presented in this paper.
                                                                                                                  7
                                        EMONSTRATIONMETHODOLOGY                                                 )
                               III. D                                                                           V 6
                                                                                                                m 5
                 A. Signal Collection & Detection                                                               (
                                                                                                                e
                                                                                                                d 4
                    The signal collection and post-collection process in [6]                                    u
                                                                                                                it3
                 was adopted for this work. All signal collections were made                                    agn2
                 using the Agilent E3238S-based RF Signal Intercept and                                         M
                 Collection System (RFSICS) that is tunable across f                              ∈               1
                                                                                            RF                    0
                 [0.02, 6.0] GHz with a WRF =36.0 MHz RF filter [17].                                               0     0.002 0.004  0.006  0.008   0.01  0.012  0.014  0.016  0.018
                 The selected frequency band is down-converted to a f                            =                                             Time (ms)
                                                                                             IF
                 70 MHz intermediate frequency (IF) and digitized by a b =                              Fig. 2. “Near-transient” region of Range Only magnitude response in Fig. 1(c)
                                                                                                        showing pre-OFDM bias.
                                                                                                     8
              RFSICSwereco-locatedinatypical office environment during                           Φ(N              2π
                                                                                                      s,n,k)= N (nŠ1)(kŠ1).                        (5)
              collection. Collections were made using NMS =6MS                                                       s
              devices operating in Ranging Only mode with subsequent burst          The desired PSD sequence {p¯(k)} is obtained by dividing (4)
              detection performed using the process in [4], i.e., the start         by the signals average power,
              of near-transient responses was located using amplitude-based                                         1
              variance trajectory (VT) at the collected SNR.                                               p¯(k)=      |S(k)|2,                    (6)
                                                                                                                   Ps
              B. Time Domain (TD) RF-DNA Fingerprinting                             where Ps is given by,
                For Time Domain (TD) fingerprinting, RF-DNA is gener-                                               Ns
              ated from sequences representing the signals instantaneous                               Ps = 1 s(n)s(n)∗,                         (7)
                                                                                                              N
              amplitude, phase, and frequency responses. Results in Sect. IV                                    s n=1
              are based on TD fingerprints (F       ) generated from N = 150
                                                TD                      s           and ∗ denotes complex conjugate. The PSD is normalized to
              near-transient samples of signal s(n)=I(n)+jQ(n).For reduce potential biasing affects due to the collection process.
              consistency with [6], [7], FTD is generated from the centered         For consistency with [3], [12], the DC term (k=0)and
              (subscript c) and normalized (over bar) amplitude {a¯c(n)},           redundant (N /2+1,N/2+2,...,N) terms are removed
                      ¯                           ¯                                               s           s                s
              phase {φc(n)} and frequency {fc(n)} sequences. Each TD                prior to SD RF-DNA fingerprint generation. Consistent with
              sequence is centered and normalized through subtraction of            the TD process in Sect. III-B, statistics are calculated over dis-
              the mean, calculated across Ns samples, and then divided by           tinct PSD regions, i.e., over contiguous sub-sequences within
              the maximum value of the centered sequence.                           {p¯(k)}. Elements of the final F           fingerprint are formed
                The F       fingerprint is generated by dividing each of the                                              SD
                       TD                                                           according to (1). For SD classification results in Sect. IV,
              TDsequencesintoN =5equallength,sequentialsubregions
                                    R                                               NR=5SDsubregionsareusedandtheresultant FSD contains
              using the process in [12]. Features are generated by calculating      a total of 24 elements (4 Statistics×6 Subregions).
              statistics, standard deviation (σ), variance (σ2), skewness (γ)
              and kurtosis (κ), over each of the N       subregions as well as      D. Gabor-Based RF-DNA Fingerprinting
                                                      R
              the N +1subregion. Where the N +1subregion consists
                    R                                R                                Previous work in [4]–[7], [12] used RF-DNA extracted in-
              of the full length of a TD sequence. The calculated statistics,       dependently from either time or frequency domain responses.
              for each of the selected subregions, are arranged as follows:         In related applications improvement has been realized by
                                             2                                      exploiting momentary and/or time localized signal energy
                              F =[σ ,σ ,γ ,k ]                 ,             (1)
                               Ri       Ri   Ri   Ri   Ri 1×4                       as a function of frequency [19]. This can be done using
              where i =1,2,...,N +1. The composite fingerprint for a                 Time-Frequency (T-F) localization whereby signal behavior
                                      R
              given TD sequence is formed by concatenating FRi from (1)             is captured and can be displayed across a T-F plane. The
              for all regions and is given by [12],                                 Discrete Gabor Transform (DGT) provides one method for
                  δ           .      .                                            T-F localization and is given by [19],
                F =            .      .                                   ,  (2)
                         FR1 . FR2 . FR3 ··· FRN +1                                                MN
                                                       R     1×4(NR+1)                                 Δ
                                                                                                               ∗                  Šj2πkn/K
              where the superscript δ denotes a specific TD sequence, i.e.,                Gmk =          s(n)W (nŠmNΔ)exp                     G,   (8)
                         ¯              ¯                                                          n=1
              {a¯c(n)}, {φc(n)} and {fc(n)}. When multiple TD sequences
                                                                         δ          where G      are the Gabor coefficients, s(n)=s(n+lMN )
              are used for RF-DNA fingerprinting (typical case), the F from                   mk                                                     Δ
                                                                                    is the periodic input signal, W(n)=W(n + lMN ) is
              (2) are concatenated to form the final TD fingerprint:                                                                              Δ
                                       .     .                                    the periodic analysis window, NΔ is the number of samples
                         FTD=         a .   φ .  f                  .        (3)    shifted, m =1,2,...,M for M total shifts, and k =
                                    F . F . F        1×4(N +1)×3
                                                           R                        0,1,...,K Š1 for K ≥ N and mod(MN ,K )=0
              For TD classification results in Sect. IV, N             =5TD                      G             G       Δ                  Δ    G
                                                                  R                 satisfied. Transformation with K        = N represents critical
              subregions are used and the resultant F          contains a total                                         G       Δ
                                                           TD                       sampling and K      >N represents oversampling [19], [20],
              of 72 elements (3 Features×4 Statistics×6 Subregions).                                 G       Δ
                                                                                    with some amount of oversampling desirable when processing
              C. Spectral Domain (SD) RF-DNA Fingerprinting                         noisy data [21], [22]. For convenience, the oversampling factor
                                                                                    is defined here as N         ≡ K /N . Given that the near-
                For Spectral Domain (SD) fingerprinting, RF-DNA is gen-                                      OS        G    Δ
              erated per the process introduced in [12] to create SD RF-            transient responses of Range Only bursts being considered
              DNA Fingerprints (F       ). The SD fingerprints are generated         here are noisy, oversampled GT processing is appropriate and
                                     SD                                             enables reliable analysis with varying SNR.
              from the power-normalized, Power Spectral Density (PSD) of              In [15], the GT is combined with the Wigner-Ville Distri-
              the near-transient sample sequence {s(n)}.GivenN near-
                                                                         s          bution (WVD) to form the Gabor-Wigner Transform (GWT).
              transient samples, the desired PSD sequence {p¯(k)} is calcu-         The GWTtakes advantage of the GTs lack of cross-terms and
              lated via a Discrete Fourier Transform (DFT) as follows [18],         faster computation as well as the higher clarity of the WVD.
                                          Ns                                        In this work the GWT is implemented as [15],
                             S(k)= 1 s(n)eŠjΦ(Ns,n,k),                      (4)                                      2.6       0.6
                                      Ns                                                              GWTmk=G WVD .                                (9)
                                          n=1                                                                         mk        mk
                                                                                 9
                                                                                                                        NR
                                                                                                                     Patches
                                     (a) Gabor Transform (GT).                                                                Fingerprint Elements
                                                                                                                                  # Statistics x NR
                                                                                               N                             σ     2  γ  κ       . . . 
                                                                                                 T                               σ 
                                                                                                                             NTxNFSamples/Patch
                                                                                                                                σ …Std Deviation
                                                                                                                                    2
                                                                                                                                  σ  …Variance
                                                                                                             NF                    γ …Skewness
                                                                                                                                   κ …Kurtosis
                                                                                         Fig. 4.  Gabor-based RF-DNA fingerprint generation using NT × NF 2-D
                                                                                         patches taken from the centered and normalized magnitude-squared GT and
                                                                                         GWTcoefficients
                                                                                         fingerprints are generated from the normalized (i.e., subtrac-
                                 (b) Gabor-Wigner Transform (GWT).                       tion of the minimum value followed by division by the max-
                                                                                         imum value) magnitude-squared Gabor (|Gmk|2) and Gabor-
              Fig. 3.  Representative Gabor-based T-F magnitude responses for a 802.16e  Wigner (|GWTmk|2) coefficients. As illustrated in Fig. 4, the
              WiMAX MS device. Responses based on near-transient responses of bursts     resultant T-F surface is subsequently divided into N            ×N
              collected during Range Only Mode at SNR =0dB.                                                                                            T       F
                                                                                         2-D subregions (patches), vectorized to a length of NTF,and
                                                                                         statistics (standard deviation (σ), variance (σ2), skewness (γ),
              The WVD in (9) is actually implemented using the Discrete                  and kurtosis (κ)) calculated. The N         ×N dimensions were
              Pseudo Wigner Distribution (DPWD) here [23] and calculated                                                           T      F
                                                                                         chosen to ensure a minimum of N              =15coefficients were
              as follows:                                                                                                         TF
                                                                                         used for statistical calculation.
                                       KG/2Š1                                               The total number of fingerprint regions is dependent on the
                                                            Šj2πkn/K
                     WVD =                         a(n)exp              G,       (10)    total number of Gabor or Gabor-Wigner coefficients generated
                            mk
                                    n=Š(KG/2Š1)                                          via (8) or (9), respectively. To facilitate comparative analysis,
                                                                                         the parameters M = 150, K              = 150 and N          =1were
                           a(n)=w(n)w∗(n)s(m+n)s∗(mŠn),                          (11)                                        G                   Δ
                                                                                         selected for generation of both the GT and GWT. Therefore, an
              where w(n) is a specific window function. Consistent                        oversampling of NOS = 150 was used in the calculation of the
              with [23], a Hamming window function was implemented.                      GTandGWT.Theresulting150×150T-Fplaneisdividedinto
                                                                                         N =50patches where N =10(m = Š75,Š74,...,75)
                 The complex IŠQ components of Gabor coefficients G                         R                             T
                                                                                  mk     and N =5(k =Š21,Š20,...,28). Similar to TD and SD
              and GWT , generated respectively in (8) and (9), can be                            F
                           mk                                                            RF-DNA fingerprint generation, statistics calculated over the
              used to compute corresponding T-F magnitude and phase                      entire 150×150 T-F plane are included representing the N
              responses. Figure 3 shows representative magnitude responses,                                                                                 R+1
              at SNR =0dB, for the GT and GWT used for generation                        subregion. For GT and GWT classification results in Sect. IV,
              of RF-DNA fingerprints and the results presented in Sect. IV.               the resultant Gabor-based RF-DNA fingerprints are comprised
              The T-F localization benefits of the GT and GWT transforms                  of a total of 204 elements (4 Statistics×51 Subregions).
              were considered for characterizing and identifying types of                E. MDA/ML Processing
              power supply disturbances [23], [24].
                 For results in Sect. IV, the GT and GWT were implemented                   As in previous work [5]–[7], [12], [25], this work uses
              using the Gaussian analysis window in (8) per [19]. RF-DNA                 Multiple Discriminant Analysis (MDA) for feature selection
                                                                                      10
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...International conference on computing networking and communications information security symposium gabor based rf dna fingerprinting for classifying e wimax mobile subscribers donald r reising michael a temple mark oxley us air force institute of technology wright patterson afb oh usa email at edu abstract previous work has demonstrated the viability motivation considering worldwide interoperability using ngerprinting to provide serial number discrim microwave access signals is provided by ination ieee wifi devices as means augment continued proliferation last mile communica conventional bit level this was done tions include long term evolution lte potential extracted from signal regions containing standard pre dened adoption an compliant solution next responses preamble midamble etc these proof concept demonstrations with generation airport communication services being pursued have shown some effectiveness providing dis faa eurocontrol civilian aviation crimination discrimination chal...

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