An interesting concept - Somebody's watching you

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    Remote physical device fingerprinting

    Tadayoshi Kohno

    CSE Department, UC San Diego

    Andre Broido

    CAIDA, UC San Diego

    kc claffy

    CAIDA, UC San Diego


    We introduce the area of remote physical device fingerprinting,

    or fingerprinting a physical device, as opposed to

    an operating system or class of devices, remotely, and without

    the fingerprinted device's known cooperation. We accomplish

    this goal by exploiting small, microscopic deviations

    in device hardware: clock skews. Our techniques do

    not require any modification to the fingerprinted devices.

    Our techniques report consistent measurements when the

    measurer is thousands of miles, multiple hops, and tens of

    milliseconds away from the fingerprinted device, and when

    the fingerprinted device is connected to the Internet from

    different locations and via different access technologies.

    Further, one can apply our passive and semi-passive techniques

    when the fingerprinted device is behind a NAT or

    firewall, and also when the device's system time is maintained

    via NTP or SNTP. One can use our techniques to

    obtain information about whether two devices on the Internet,

    possibly shifted in time or IP addresses, are actually the

    same physical device. Example applications include: computer

    forensics; tracking, with some probability, a physical

    device as it connects to the Internet from different public access

    points; counting the number of devices behind a NAT

    even when the devices use constant or random IP IDs; remotely

    probing a block of addresses to determine if the addresses

    correspond to virtual hosts, e.g., as part of a virtual

    honeynet; and unanonymizing anonymized network traces.

    1 Introduction

    There are now a number of powerful techniques for remote

    operating system fingerprinting, i.e., techniques for

    remotely determining the operating systems of devices on

    the Internet [2, 3, 5, 27]. We push this idea further and introduce

    the notion of remote physical device fingerprinting,

    or remotely fingerprinting a physical device, as opposed to

    an operating system or class of devices, without the fingerprinted

    device's known cooperation. We accomplish this

    goal to varying degrees of precision by exploiting microscopic

    deviations in device hardware: clock skews.


    three main classes of remote physical device fingerprinting

    techniques: passive, active, and semi-passive. The

    first two have standard definitions - to apply a passive

    fingerprinting technique, the fingerprinter (measurer, attacker,

    adversary) must be able to observe traffic from the

    device (the fingerprintee) that the attacker wishes to fingerprint,

    whereas to apply an active fingerprinting technique,

    the fingerprinter must have the ability to initiate connections

    to the fingerprintee. Our third class of techniques,

    which we refer to as semi-passive fingerprinting techniques,

    assumes that after the fingerprintee initiates a connection,

    the fingerprinter has the ability to interact with the fingerprintee

    over that connection; e.g., the fingerprinter is a website

    with which the device is communicating, or is an ISP

    in the middle capable of modifying packets en route. Each

    class of techniques has its own advantages and disadvantages.

    For example, passive techniques will be completely

    undetectable to the fingerprinted device, passive and semipassive

    techniques can be applied even if the fingerprinted

    device is behind a NAT or firewall, and semi-passive and

    active techniques can potentially be applied over longer periods

    of time; e.g., after a laptop connects to a website and

    the connection terminates, the website can still continue to

    run active measurements.

    METHODOLOGY. For all our methods, we stress that the

    fingerprinter does not require any modification to or cooperation

    from the fingerprintee; e.g., we tested our techniques

    with default Red Hat 9.0, Debian 3.0, FreeBSD

    5.2.1, OpenBSD 3.5, OS X 10.3.5 Panther, Windows XP

    SP2, andWindows for Pocket PC 2002 installations.1 In Table

    1 we summarize our preferred methods for fingerprinting

    the most popular operating systems.

    Our preferred passive and semi-passive techniques exploit

    the fact that most modern TCP stacks implement the

    1Our techniques work for the default installs of other versions of these

    operating systems; here we just mention the most recent stable versions of

    the operating systems that we analyzed.

    Technique and section Class NTP Red Hat 9.0 OS X Panther Windows XP

    TCP timestamps, Section 3 passive Yes Yes Yes No

    TCP timestamps, Section 3 semi-passive Yes Yes Yes Yes

    ICMP tstamp requests, Section 4 active No Yes No Yes

    Table 1. This table summarizes our main clock skew-based physical device
    fingerprinting techniques.

    A "Yes" in the NTP column means that one can use the attack regardless of
    whether the fingerprintee

    maintains its system time with NTP [19]. One can use passive and
    semi-passive techniques when

    the fingerprintee is behind a NAT or current generation firewall.

    TCP timestamps option from RFC 1323 [13] whereby, for

    performance purposes, each party in a TCP flow includes

    information about its perception of time in each outgoing

    packet. A fingerprinter can use the information contained

    within the TCP headers to estimate a device's clock skew

    and thereby fingerprint a physical device. We stress that

    one can use our TCP timestamps-based method even when

    the fingerprintee's system time is maintained via NTP [19].

    While most modern operating systems enable the TCP

    timestamps option by default, Windows 2000 and XP machines

    do not. Therefore, we developed a trick, which involves

    an intentional violation of RFC 1323 on the part of

    a semi-passive or active adversary, to convince Microsoft

    Windows 2000 and XP machines to use the TCP timestamps

    option in Windows-initiated flows. In addition to

    our TCP timestamps-based approach, we consider passive

    fingerprinting techniques that exploit the difference in time

    between how often other periodic activities are supposed to

    occur and how often they actually occur, and we show how

    one might use a Fourier transform on packet arrival times

    to infer a device's clock skew. Since we believe that our

    TCP timestamps-based approach is currently our most general

    passive technique, we focus on the TCP timestamps

    approach in this paper.

    An active adversary could also exploit the ICMP protocol

    to fingerprint a physical device. Namely, an active adversary

    could issue ICMP Timestamp Request messages to

    the fingerprintee and record a trace of the resulting ICMP

    Timestamp Reply messages. If the fingerprintee does not

    maintain its system time via NTP or does so only infrequently

    and if the fingerprintee replies to ICMP Timestamp

    Requests, then an adversary analyzing the resulting ICMP

    Timestamp Reply messages will be able to estimate the fingerprintee's

    system time clock skew. Default Red Hat 9.0,

    Debian 3.0, FreeBSD 5.2.1, OpenBSD 3.5, and Windows

    2000 and XP and Pocket PC 2002 installations all satisfy

    the above preconditions.

    PARAMETERS OF INVESTIGATION. Toward developing the

    area of remote physical device fingerprinting via remote

    clock skew estimation, we must address the following set

    of interrelated questions:

    (1) For what operating systems are our remote clock skew

    estimation techniques applicable?

    (2) What is the distribution of clock skews acrossmultiple

    fingerprintees? And what is the resolution of our clock

    skew estimation techniques? (I.e., can one expect two

    machines to have measurably different clock skews?)

    (3) For a single fingerprintee, can one expect the clock

    skew estimate of that fingerprintee to be relatively

    constant over long periods of time, and through reboots,

    power cycles, and periods of down time?

    (4) What are the effects of a fingerprintee's access technology

    (e.g., wireless, wired, dialup, cable modem)

    on the clock skew estimates for the device?

    (5) How are the clock skew estimates affected by the distance

    between the fingerprinter and the fingerprintee?

    (6) Are the clock skew estimates independent of the fingerprinter?

    I.e., when multiple fingerprinters aremeasuring

    a single fingerprintee at the same time, will they

    all output (approximately) the same skew estimates?

    (7) How much data do we need to be able to remotely

    make accurate clock skew estimates?

    Question (6) is applicable because common fingerprinters

    will probably use NTP-based time synchronization when

    capturing packets, as opposed to more precise CDMA- or

    GPS-synchronized timestamps. Answers to the above questions

    will help determine the efficacy of our physical device

    fingerprinting techniques.


    and refine our techniques, we conducted experiments

    with machines that we controlled and that ran a variety of

    operating systems, including popular Linux, BSD, and Microsoft

    distributions. In all cases we found that we could

    use at least one of our techniques to estimate clock skews

    of the machines, and that we required only a small amount

    of data, though the exact data requirements depended on the

    operating system in question. For the most popular operating

    systems, we observed that when the system did not use

    NTP- or SNTP-based time synchronization, then the TCP

    timestamps-based and the ICMP-based techniques yielded

    approximately the same skew estimates. This result, coupled

    with details that we describe in the body, motivated

    us to use the TCP timestamps-based method in most of our

    experiments. We survey some of our experiments here.

    To understand the effects of topology and access technology

    on our skew estimates, we fixed the location of the

    fingerprinter and applied our TCP timestamps-based technique

    to a single laptop in multiple locations, on both North

    American coasts, from wired, wireless, and dialup locations,

    and from home, business, and campus environments

    (Table 3). All clock skew estimates for the laptop were

    close- the difference between the maximum and the minimum

    skew estimate was only 0.67 ppm. We also simultaneously

    measured the clock skew of the laptop and another

    machine from multiple PlanetLab nodes throughout

    the world, as well as from a machine of our own with a

    CDMA-synchronized Dag card [1, 9, 11, 17] for taking network

    traces with precise timestamps (Table 4). With the exception

    of the measurements taken by a PlanetLab machine

    in India (over 300 ms round trip time away), for each experiment,

    all the fingerprinters (in North America, Europe, and

    Asia) reported skew estimates within only 0.56 ppm of each

    other. These experiments suggest that, except for extreme

    cases, the results of our clock skew estimation techniques

    are independent of access technology and topology.

    Toward understanding the distribution of clock skews

    across machines, we applied the TCP timestamps technique

    to the devices in a trace collected on one of the U.S.'s Tier 1

    OC-48 links (Figure 2). We also measured the clock skews

    of 69 (seemingly) identical Windows XP SP1 machines in

    one of our institution's undergraduate computing facilities

    (Figure 3). The latter experiment, which ran for 38 days,

    as well as other experiments, show that the clock skew estimates

    for any given machine are approximately constant

    over time, but that different machines have detectably different

    clock skews. Lastly, we use the results of these and

    other experiments to argue that the amount of data (packets

    and duration of data) necessary to perform our skew estimation

    techniques is low, though we do not perform a rigorous

    analysis of exactly what "low" means.


    the applicability of our techniques, we applied our techniques

    to a honeyd [24] virtual honeynet consisting of 100

    virtual Linux 2.4.18 hosts and 100 virtualWindows XP SP1

    hosts. Our experiments showed with overwhelming probability

    that the TCP flows and ICMP timestamp responses

    were all handled by a single machine as opposed to 200

    different machines. We also applied our techniques to a

    network of five virtual machines running under VMware

    Workstation [4] on a single machine. In this case, the clock

    skew estimates of the virtual machines are significantly different

    from what one would expect from real machines (the

    skews were large and not constant over time; Figure 5). An

    application of our techniques, or natural extensions, might

    therefore be to remotely detect virtual honeynets.

    Another applications of our techniques is to count the

    number of hosts behind a NAT, even if those hosts use random

    or constant IP IDs to counter Bellovin's attack [7],

    even if all the hosts run the same operating system, and even

    if not all of the hosts are up at the same time. Furthermore,

    when both our techniques and Bellovin's techniques are applicable,

    we expect our approach to provide a much higher

    degree of resolution. One could also use our techniques for

    forensics purposes, e.g., to argue whether or not a given laptop

    was connected to the Internet from a given access location.

    One could also use our techniques to help track laptops

    as they move, perhaps as part of a Carnivore-like project

    (here we envision our skew estimates as one important component

    of the tracking; other components could be information

    gleaned from existing operating system fingerprinting

    techniques, usage characteristics, and other heuristics). One

    can also use our techniques to catalyze the unanonymization

    of prefix-preserving anonymized network traces [28, 29].


    known that seemingly identical computers can have disparate

    clock skews. The NTP [19] specification describes

    a method for reducing the clock skews of devices' system

    clocks, though over short periods of time an NTPsynchronized

    machine may still have slight clock skew. In

    1998 Paxson [22] initiated a line of research geared toward

    eliminating clock skew from network measurements, and

    one of the algorithms we use is based on a descendent of

    the Paxson paper by Moon, Skelly, and Towsley [20]. Further

    afield, though still related to clock skews, P´asztor and

    Veitch [21] have created a software clock on a commodity

    PC with high accuracy and small clock skew. One fundamental

    difference between our work and previous work

    is our goal: whereas all previous works focus on creating

    methods for eliminating the effects of clock skews, our

    work exploits and capitalizes on the effects of clock skews.

    Anagnostakis et. al. [6] use ICMP Timestamp Requests

    to study router queuing delays. It is well known that a network

    card's MAC address is supposed to be unique and

    therefore could serve as a fingerprint of a device assuming

    that the adversary can observe the device's MAC address

    and that the owner of the card has not changed the

    MAC address. The main advantage of our techniques over

    a MAC address-based approach is that our techniques are

    mountable by adversaries thousands of miles and multiple

    hops away. One could also use cookies or any other persistent

    data to track a physical device, but such persistent

    data may not always be available to an adversary, perhaps

    because the user is privacy-conscious and tries to minimize

    storage and transmission of such data, or because the user

    never communicates that data unencrypted.

    See [15] for the full version of this paper.

    2 Clocks and clock skews

    When discussing clocks and clock skews, we build on

    the nomenclature from the NTP specification [19] and from

    Paxson [22]. A clock C is designed to represent the amount

    of time that has passed since some initial time i[C]. Clock

    C's resolution, r[C], is the smallest unit by which the clock

    can be incremented, and we refer to each such increment

    as a tick. A resolution of 10 ms means that the clock is designed

    to have 10ms granularity, not that the clock is always

    incremented exactly every 10 ms. Clock C's intended frequency,

    Hz[C], is the inverse of its resolution; e.g., a clock

    with 10 ms granularity is designed to run at 100 Hz. For

    all t ? i[C], let R[C](t) denote the time reported by clock

    C at time t, where t denotes the true time as defined by

    national standards. The offset of clock C, off[C], is the difference

    between the time reported by C and the true time,

    i.e., off[C](t) = R[C](t) ? t for all t ? i[C]. A clock's

    skew, s[C], is the first derivative of its offset with respect to

    time, where we assume for simplicity of notation that R[C]

    is a differentiable function in t. We report skew estimates in

    microseconds per second (?s/s) or, equivalently, parts per

    million (ppm). As we shall show, and as others have also

    concluded [22, 20, 26], it is often reasonable to assume that

    a clock's skew is constant. When the clock in question is

    clear from context, we shall remove the parameter C from

    our notation; e.g., s[C] becomes s.

    A given device can have multiple, possibly independent,

    clocks. For remote physical device fingerprinting, we exploit

    two different clocks: the clock corresponding to a device's

    system time, and a clock internal to a device's TCP

    network stack, which we call the device's TCP timestamps

    option clock or TSopt clock. We do not consider the hardware

    bases for these clocks here since our focus is not on

    understanding why these clocks have skews, but on exploiting

    the fact these clocks can have measurable skews on popular

    current-generation systems.

    THE SYSTEM CLOCK. To most users of a computer system,

    the most visible clock is the device's system clock,

    Csys, which is designed to record the amount of time since

    00:00:00 UTC, January 1, 1970. Although the system

    clocks on professionally administered machines are often

    approximately synchronized with true time via NTP [19]

    or, less accurately, via SNTP [18], we stress that it is much

    less likely for the system clocks on non-professionally managed

    machines to be externally synchronized. This lack of

    synchronization is because the default installations of most

    of the popular operating systems that we tested do not synchronize

    the hosts' system clocks with true time or, if they

    do, they do so only infrequently. For example, defaultWindows

    XP Professional installations only synchronize their

    system times with Microsoft's NTP server when they boot

    and once a week thereafter. Default Red Hat 9.0 Linux

    installations do not use NTP by default, though they do

    present the user with the option of entering an NTP server.

    Default Debian 3.0, FreeBSD 5.2.1, and OpenBSD 3.5 systems,

    at least under the configurations that we selected (e.g.,

    "typical user"), do not even present the user with the option

    of installing ntpd. For such a non-professionallyadministered

    machine, if an adversary can learn the values

    of the machine's system clock at multiple points in time,

    the adversary will be able to infer information about the device's

    system clock skew, s[Csys].


    specifies the TCP timestamps option to the TCP protocol.

    A TCP flow will use the TCP timestamps option if the network

    stacks on both ends of the flow implement the option

    and if the initiator of the flow includes the option in the

    initial SYN packet. All modern operating systems that we

    tested implement the TCP timestamps option. Of the systems

    we tested, Microsoft Windows 2000 and XP are the

    only ones that do not include the TCP timestamps option in

    the initial SYN packet (MicrosoftWindows Pocket PC 2002

    does include the option when initiating TCP flows). In Section

    3 we introduce a trick for making Windows 2000- and

    XP-initiated flows use the TCP timestamps option.

    For physical device fingerprinting, the most important

    property of the TCP timestamps option is that if a flow uses

    the option, then a portion of the header of each TCP packet

    in that flow will contain a 32-bit timestamp generated by

    the creator of that packet. The RFC does not dictate what

    values the timestamps should take, but does say that the

    timestamps should be taken from a "virtual clock" that is "at

    least approximately proportional to real time [13];" the RFC

    1323 PAWS algorithmdoes stipulate (Section 4.2.2) that the

    resolution of this virtual clock be between 1 ms and 1 second.

    We refer to this "virtual clock" as the device's TCP

    timestamps option clock, or its TSopt clock Ctcp. There is no

    requirement that a device's TSopt clock and its system clock

    be correlated. Moreover, for popular operating systems like

    Windows XP, Linux, and FreeBSD, a device's TSopt clock

    may be unaffected by adjustments to the device's system

    clock via NTP. To sample some popular operating systems,

    standard Red Hat 9.0 and Debian 3.0 Linux distributions2

    and FreeBSD 5.2.1 machines have TSopt clocks with 10 ms

    resolution, OS X Panther and OpenBSD 3.5 machines have

    TSopt clocks with 500 ms resolution, and Microsoft Windows

    2000, XP, and Pocket PC 2002 systems have TSopt

    clocks with 100 ms resolution. Most systems reset their

    TSopt clock to zero upon reboot; on these systems i[Ctcp]

    is the time at which the system booted. If an adversary can

    learn the values of a device's TSopt clock at multiple points

    in time, then the adversary may be able to infer information

    about the device's TSopt clock skew, s[Ctcp].

    2We do not generalize this to all Linux distributions since Knoppix 3.6,

    with the 2.6.7 experimental kernel, has 1 ms resolution.

    3 Exploiting the TCP Timestamps Option

    In this section we consider (1) how an adversary might

    obtain samples of a device's TSopt clock at multiple points

    in time and (2) how an adversary could use those samples

    to fingerprint a physical device. We assume for now that

    there is a one-to-one correspondence between physical devices

    and IP addresses, and defer to Section 8 a discussion

    of how to deal with multiple active hosts behind a NAT; in

    this section we do consider NATs with a single active device

    behind them.

    THE MEASURER. The measurer can be any entity capable

    of observing TCP packets from the fingerprintee, assuming

    that those packets have the TCP timestamps option enabled.

    The measurer could therefore be the fingerprintee's

    ISP, or any tap in the middle of the network over which

    packets from the device travel; e.g., we apply our techniques

    to a trace taken on a major Tier 1 ISP's backbone OC-48

    links. The measurer could also be any system with which

    the fingerprintee frequently communicates; prime examples

    of such systems include a search engine like Google, a news

    website, and a click-through ads service that displays content

    on a large number of websites. If the measurer is active,

    then the measurer could also be the one to initiate a

    TCP flow with the fingerprintee, assuming that the device

    is reachable and has an open port. If the measurer is semipassive

    or active, then it could make the flows that it observes

    last abnormally long, thereby giving the measurer

    samples of the fingerprintee's clock over extended periods

    of time.


    We seek the ability to measure TSopt clock skews

    of Windows 2000 and XP machines even if those machines

    are behind NATs and firewalls. But, because of the nature of

    NATs and firewalls, in these cases we will typically be limited

    to analyzing flows initiated by the Windows machines.

    Unfortunately, becauseWindows 2000 and XP machines do

    not include the TCP timestamps option in their initial SYN

    packets, the TCP timestamps RFC [13] mandates that none

    of the subsequent packets in Windows-initiated flows can

    include the TCP timestamps option. Thus, assuming that all

    parties correctly implement the TCP RFCs, a passive adversary

    will not be able to exploit the TCP timestamps option

    with Windows 2000/XP-initiated flows.

    If the adversary is semi-passive, we observe the following

    trick. Assume for simplicity that the adversary is the device

    to whom theWindows machine is connecting. After receiving

    the initial SYN packet from the Windows machine,

    the adversary will reply with a SYN/ACK, but the adversary

    will break the RFC 1323 specification and include the TCP

    timestamps option in its reply. After receiving such a reply,

    our Windows 2000 and XP machines ignored the fact that

    they did not include the TCP timestamps option in their initial

    SYN packets, and included the TCP timestamps option

    in all of their subsequent packets. As an extension, we note

    that the adversary does not have to be the device to whom

    the Windows machine is connecting. Rather, the adversary

    simply needs to be able to mount a "device-in-the-middle"

    attack and modify packets such that the Windows machine

    receives one with the TCP timestamps option turned on. If

    the adversary is the device's ISP, then the ISP could rewrite

    theWindows machine's initial SYN packets so that they include

    the TCP timestamps option. The SYN/ACKs from

    the legitimate recipients will therefore have the TCP timestamps

    option enabled and, from that point forward, theWindowsmachinewill

    include the TCP timestamps option in all

    subsequent packets in the flows.

    We applied this technique to Windows XP machines on

    a residential cable system with a LinkSys Wireless Access

    Point and a NAT, as well as to Windows XP SP2 machines

    using the default XP SP2 firewall, and to Windows XP SP1

    machines with the Windows ZoneAlarm firewall. (While

    current firewalls do not detect this trick, it is quite possible

    that future firewalls might.)


    that an adversary has obtained a trace T of TCP packets

    from the fingerprintee, and let us assume for simplicity

    that all |T | packets in the trace have the TCP timestamps

    option enabled. Toward estimating a device's TSopt clock

    skew s[Ctcp] we adopt the following additional notation. Let

    ti be the time in seconds at which the measurer observed the

    i-th packet in T and let Ti be the Ctcp timestamp contained

    within the i-th packet. Define

    xi = ti ? t1

    vi = Ti ? T1

    wi = vi/Hz

    yi = wi ? xi

    OT = { (xi, yi) : i ? {1, . . . , |T |} } .

    The unit for wi is seconds; yi is the observed offset of the ith

    packet; OT is the the offset-set corresponding to the trace

    T . We discuss below how to compute Hz if it is not known

    to the measurer in advance. As an example, Figure 1 shows

    the offset-sets for two devices in a two-hour trace of traffic

    from an Internet backbone OC-48 link on 2004-04-28 (we

    omit IP addresses for privacy reasons). Shifting the clocks

    by t1 and T1 for xi and vi is not necessary for our analysis

    but makes plots like in Figure 1 cleaner.

    If we could assume that the measurer's clock is accurate

    and that the t values represent true time, and if we could assume

    that there is no delay between when the fingerprintee

    generates the i-th packet and when the measurer records

    the i-th packet, then yi = off(xi + t1). Under these assumptions,

    and if we make the additional assumption that

    0 900 1800 2700 3600 4500 5400 6300 7200

    time since start of measurement (seconds)







    observed offset (ms)

    Source 1: 10Hz TSopt clock, 37526 packets, ttl=113

    Source 2: 100Hz TSopt clock, 20974 packets, ttl=55

    linear programming-based upper bound

    Figure 1. TSopt clock offset-sets for two

    sources in BBN. Trace recorded on an OC-

    48 link of a U.S. Tier 1 ISP, 2004-04-28 19:30-

    21:30PDT. The source with the wide band has

    a 10 Hz TSopt clock, the source with the narrow

    band has a 100 Hz TSopt clock. A source

    with no clock skew would have a horizontal


    R is differentiable, then the first derivative of y, which is

    the slope of the points in OT , is the skew s of Ctcp. Since

    we cannot generally make these assumptions, we are left to

    approximate s from the data.

    Let us consider plots like those in Figure 1 more closely.

    We first observe that the large band corresponds to a device

    where the TSopt clock has low resolution (r = 100 ms) and

    that the narrow band corresponds to a device with a higher

    resolution (r = 10 ms). The width of these bands, and in

    particular the wide band, means that if the duration of our

    trace is short, we cannot always approximate the slope of

    the points in OT by computing the slope between any two

    points in the set. Moreover, as Paxson and others have noted

    in similar contexts [22, 20], variable network delay renders

    simple linear regression insufficient. Consequently, to approximate

    the the skew s from OT , we borrow a linear programming

    solution from Moon, Skelly, and Towsley [20],

    which has as its core Graham's convex hull algorithm on

    sorted data [12].

    The linear programming solution outputs the equation of

    a line ?x + ? that upper-bounds the set of points OT. We

    use an upper bound because network and host delays are all

    positive. The slope of the line, ?, is our estimate of the clock

    skew of Ctcp. In detail, the linear programming constraints

    for this line are that, for all i ? {1, . . . , |T |},

    ? · xi + ? ? yi ,

    which means that the solution must upper-bound all the

    points in OT . The linear programming solution then minimizes

    the average vertical distance of all the points in OT

    from the line; i.e., the linear programming solution is one

    that minimizes the objective function


    |T | ·



    ? · xi + ? ? yi .

    Although one can solve the above using standard linear programming

    techniques, as Moon, Skelly, and Towsley [20]

    note, there exist techniques to solve linear programming

    problems in two variables in linear time [10, 16]. We use

    a linear time algorithm in all our computations.

    It remains to discuss how to infer Hz if themeasurer does

    not know it in advance. One solution involves computing

    the slope of the points

    I = { (xi, vi) : i ? {1, . . . , |T | }

    and rounding to the nearest integer. One can compute the

    slope of this set by adapting the above linear programming

    problem to this set.

    AN EQUIVALENT VIEW. If A is the slope of the points in

    the above set I, derived using the linear programming algorithm,

    then one could also approximate the skew of Ctcp

    as A/Hz ? 1. This approach is simply a different way of

    arriving at the same solution since we can prove that, when

    using the linear programming method for slope estimation,

    both approaches produce the same skew estimate. We use

    the offset-set approach since these sets naturally yield figures

    where the skews are clearly visible; e.g., Figure 1.

    4 Exploiting ICMP Timestamp Requests

    THE MEASURER. To exploit a device's system time clock

    skew, the measurer could be any website with which the fingerprintee

    communicates, or any other device on the Internet

    provided that the measurer is capable of issuing ICMP

    Timestamp Requests (ICMP message type 13) to the fingerprintee.

    The measurer must also be capable of recording

    the fingerprintee's subsequent ICMP Timestamp Reply

    messages (ICMP message type 14). In order for this technique

    to be mountable, the primary limitation is that the device

    must not be behind a NAT or firewall that filters ICMP.


    that an adversary has obtained a trace T of ICMP

    Timestamp Reply messages from the fingerprintee. The

    ICMP Timestamp Reply messages will contain two 32-bit

    values generated by the fingerprintee. The first value is

    the time at which the corresponding ICMP Timestamp Request

    packet was received, and the second value is the time

    at which the ICMP Timestamp Reply was generated; here

    time is according to the fingerprintee's system clock, Csys,

    and is reported in milliseconds since midnight UTC. Windows

    machines report the timestamp in little endian format,

    whereas all the other machines that we tested report

    the timestamp in big endian notation. The remaining notation

    and the method for skew estimation is now identical to

    what we presented in Section 3, with two minor exceptions.

    First, the adversary does not have to compute Hz since RFC

    792 [23] requires that Hz be 1000 (or, if not, that a special

    bit be set to indicate non-compliance). Second, since

    the time reported in the ICMP Timestamp Reply is in milliseconds

    since midnight UTC, we expect the timestamps

    reported in the ICMP Timestamp Reply messages to reset

    approximately once a day; we adjust the v values accordingly.

    (The only thing special that our attack exploits about

    ICMP is the fact that ICMP has a message type that will

    reveal a device's system time; our techniques would work

    equally well with any other protocol that leaks information

    about a device's system or other clock.)


    of the rest of this paper, we focus on our TCP timestampsbased

    approach for physical device fingerprinting rather

    than our ICMP-based approach. This is not because we

    consider the ICMP-based approach to be inferior. Rather,

    we focus on the TCP timestamps-based approach because

    most systems have TSopt clocks that operate at a lower

    frequency than the 1000 Hz clocks included in the ICMP

    timestamp reply messages. This means that it should require

    less data for an active adversary to mount our ICMP

    fingerprinting technique than to mount our TCP timestamps

    technique. Our positive results for the TCP timestampsbased

    fingerprinting techniques imply that the ICMP-based

    fingerprinting technique will be effective and will have low

    data requirements. Focusing on our TCP timestamps based

    approach also allows us to experiment with machines behind

    NATs and firewalls. We also remark that for popular

    operating systems, if a system does not externally synchronize

    its system time, then the system's TSopt and system

    clocks will be highly correlated (Section 7), which means

    that the distribution of system clock skews for machines not

    using NTP will be similar to the distribution of TSopt clocks


    5 Distribution and stability of TSopt clock

    skew measurements

    We now address two fundamental properties that must

    hold in order for remote clock skew estimation to be an

    effective physical device fingerprinting technique. First,

    we show that there is variability in different devices' clock

    skews, meaning that it is reasonable to expect different devices

    on the Internet to have measurably different clock

    skews. Second, we give evidence to suggest that clock

    skews, as measured by our techniques, are relatively constant

    over time. These two facts provide the basis for our

    min pkts min duration total sources with entropy

    per hour per hour sources stable skews (bits)

    (mp) (md, mins) (|S|) (|S|)

    0 10 18335 8225 4.87

    0 30 13517 6859 5.39

    0 50 7246 4120 5.87

    500 10 4356 2583 5.99

    500 30 4016 2446 6.11

    500 50 3368 2104 6.18

    2000 10 1730 1116 6.22

    2000 30 1629 1077 6.32

    2000 50 1489 1009 6.41

    Table 2. Entropy estimates from BB-2004-04-

    28 when pv = 1 ppm.

    use of remote clock skew estimation as a physical device

    fingerprinting technique since they imply that an adversary

    can gain (sometimes significant) information by applying

    our techniques to measure a device's or set of devices' clock


    The novelty here is not in claiming that these properties

    are true. Indeed, it is well known that different computer

    systems can have different clock skews, and others

    [22, 20, 21, 26] have argued that a given device generally

    has a constant clock skew. Rather, the contribution here is

    showing that these properties survive our remote clock skew

    estimation techniques and, in the case of our analyses of the

    distribution of clock skews, measuring the bits of information

    (entropy) a passive adversary might learn by passively

    measuring the TSopt clock skews of fingerprintees.


    TRACES. Our first experiment in this section focuses

    on understanding the distribution of clock skews across devices

    as reported by our TCP timestamps-based passive fingerprinting

    technique. For this experiment we analyzed a

    passive trace of traffic in both directions of a major OC-48

    link; CAIDA collected the trace between 19:30 and 21:30

    PDT on 2004-04-28. Since the OC-48 link runs North-

    South, let BBN denote the Northbound trace, and let BBS

    denote the Southbound trace (BB stands for backbone).

    CAIDA obtained the traces using different Dag [11] cards in

    each direction; these cards' clocks were synchronized with

    each other, but not with true time. This latter property does

    not affect the following discussion because (1) the clock

    skews of the Dag cards appear to be constant and therefore

    only shift our skew estimates by a constant amount and (2)

    here we are only interested in the general distribution of the

    clock skews of the sources in the traces.

    Let mp and md be positive integers. For simplicity, fix

    BB = BBN or BBS. Also assume for simplicity that BB

    only contains TCP packets with the TCP timestamps option

    turned on. Recall that the trace BB last for two hours. At

    a high-level, our analysis considers the set S of sources in

    BB that have ? mp packets in both the first and the second

    hours, and where the differences in time between the

    source's first and last packets in each hour are ? md minutes.

    If mp and md are large, then the sources in S all generate

    a large number of packets, and over a long period of


    For each source in S, we apply our clock skew estimation

    technique from Section 3 to the full trace, the first hour

    only, and the second hour only. Let pv be a positive number,

    and let S be the subset of S corresponding to the sources

    whose skew estimates for the full trace, the first hour, and

    the second hour are all within pv ppm of each other, and

    whose intended frequency Hz is one of the standard values

    (1, 2, 10, 100, 512, 1000). If pv is small, then we are

    inclined to believe that the skew estimates for the sources

    in S closely approximate the true skews of the respective

    sources. Table 2 shows values of |S| and |S| for different

    values of mp and md and when pv = 1 ppm.

    The value |S|/|S| gives an indication of the ratio of

    sources of which we can accurately (within pv ppm) measure

    the clock skew. While useful, this value provides little

    information about the actual distribution of the clock skew

    estimates. Much more (visually) telling are images such as

    Figure 2, which shows a histogram of the skew estimates

    (for the full two hour trace) for all the sources in S when

    mp = 2000, md = 50 minutes, and pv = 1 ppm. (The true

    histogram may be shifted horizontally based on the clock

    skew of the Dag cards, but a horizontal shift does not affect

    the general shape of the distribution.) Empirically, for

    any given values for mp, md, and pv, we can compute the

    entropy of the distribution of clock skews. Doing so serves

    as a means of gauging how many bits of information an

    adversary might learn by passively monitoring a device's

    clock skew, assuming that devices' clock skews are constant

    over time, which is something we address later. To

    compute the entropy, we consider bins of width pv, and for

    each source s in S, we increment the count of the bin corresponding

    to devices with clock skews similar to the skew

    of s (here we use the skew estimate computed over full two

    hours). We then allocate another bin of size |S| ? |S|; this

    bin counts the number of sources that do not have consistent

    clock skew measurements. We apply the standard entropy

    formula [25] to compute the entropy of this distribution

    of bins, the results of which appear in the last column

    of Table 2. As one might expect, the amount of information

    available to an adversary increases as mp and md increase.

    Assuming that clock skews are constant over time, our

    results suggest that a passive adversary could learn at least

    six bits of information about a physical device by applying

    our techniques from Section 3. More bits of information

    -300 -200 -100 0 100 200 300

    skew estimate (ppm)








    Figure 2. Histogram of TSopt clock skew estimates

    for sources in BBN. Trace recorded

    on an OC-48 link of a U.S. Tier 1 ISP, 2004-04-

    28 19:30-21:30PDT. Here mp = 2000 packets,

    md = 50 minutes, and pv = 1 ppm.

    should be available to an active adversary since an active

    adversary might be able to force the fingerprintee to send

    packets more frequently or over longer periods of time.


    A HOMOGENEOUS LAB. One observation on the above

    analysis is that we applied it to a wide variety of machines,

    which likely ran a wide variety of operating systems. Therefore,

    one may wonder whether the distribution shown in

    Figure 2 is due to operating system differences or to actual

    physical differences on the devices. For example, given

    only the above results, it might still be possible to argue

    that if we applied our skew estimates to a large number

    of (apparently) homogeneous machines, we would get back

    approximately the same (i.e., indistinguishable) skew estimates

    for all of the machines. To address this issue, we conducted

    an experiment with 69 (apparently) homogeneous

    machines in one of UCSD's undergraduate computing laboratories.

    All the machines were Micron PCs with 448MHz

    Pentium II processors running MicrosoftWindows XP Professional

    Service Pack 1. Our measurer, host2, was a

    Dell Precision 410 with a 448MHz Pentium III processor

    and running Debian 3.0 with a recompiled 2.4.18 kernel;

    host2 is located within the University's computer science

    department and is 3 hops and a half amillisecond away from

    the machines in the undergraduate laboratory.

    To create the requisite trace of TCP packets from these

    machines, we repeatedly opened and then closed connections

    from host2 to each of these machines. Each openthen-

    close resulted in the Windows machines sending two

    packets to host2 with the TCP timestamps option turned

    on (the Windows machine sent three packets for each flow,

    but the TCP timestamp was always zero in the first of these

    three packets). Because of our agreement with the administrators

    of these machines, we were only able to open and

    close connections with these Windows machines at random

    intervals between zero and five minutes long. Thus, on average

    we would expect to see each machine send host2

    48 TCP packets with the TCP timestamps option turned

    on per hour. The experiment lasted for 38 days, beginning

    at 19:00PDT 2004-09-07 and ending at approximately

    20:30PDT 2004-10-15.

    Figure 3 shows a plot, similar to Figure 1, for the 69

    Micron machines as measured by host2 but sub-sampled

    to one out of every two packets. Note that the plot uses

    different colors for the observed offsets for different machines

    (colors are overloaded). Since the slopes of the sets

    of points for a machine corresponds to the machine's skew,

    this figure clearly shows that different machines in the lab

    have measurably different clock skews. Thus, we can easily

    distinguish some devices by their clock skews (for other

    devices, we cannot). Because Windows XP machines reset

    their TSopt clocks to zero when they reboot, some of

    the diagonal lines seem to disappear several days into the

    figure. Our algorithms handle reboots by recalibrating the

    initial observed offset, though this recalibration is not visible

    in Figure 3. The time in Figure 3 begins on 8:30PDT

    2004-09-10 (Friday) specifically because the administrators

    of the lab tend to reboot machines around 8:00PDT, and

    beginning the plot on Friday morning means that there are

    fewer reboots in the figure. We consider this experiment in

    more detail below, where our focus is on the stability of our

    clock skew estimates.

    STABILITY OF CLOCK SKEWS. We now consider the stability

    of the TSopt clock skews for the devices in the abovementioned

    undergraduate laboratory. Consider a single machine

    in the laboratory. We divide the trace for this machine

    into 12- and 24-hour periods, discarding 12-hour periods

    with less than 528 packets from the device, and discarding

    24-hour periodswith less than 1104 packets from the device

    (doing so corresponds to discarding 12-hour periods when

    the device is not up for at least approximately 11 hours, and

    discarding 24-hour periods that the device is not up for at

    least 23 hours). We compute the device's clock skew for

    each non-discarded period, and then compute the difference

    between the maximum and minimum estimates for the nondiscarded

    periods. This value gives us an indication of the

    stability of the device's clock skew.

    For 12-hour periods, the maximum difference for a single

    device in the lab ranged between 1.29 ppm and 7.33

    ppm, with a mean of 2.28 ppm. For 24-hour periods, the

    maximum difference for a single device ranged between

    0.01 ppm and 5.32 ppm, with a mean of 0.71 ppm. Interestingly,

    there seems to have been some administrator

    function at 8:00PDT on 2004-09-10 that slightly adjusted

    0 12 24 36 48 60 72 84 96

    time since start of measurement (hours)











    observed offset (seconds)

    Figure 3. TSopt clock offset-sets for 69

    Micron 448MHz Pentium II machines running

    Windows XP Professional SP1. Trace

    recorded on host2, three hops away, 2004-

    09-10 08:30PDT to 2004-09-14 08:30PDT.

    the TSopt clock skews of some of the machines. If we conduct

    the same analysis for the trace beginning at 8:30PDT

    2004-09-10 and ending on 2004-10-15, for 24-hour periods,

    the range for maximum difference for each device in the lab

    dropped to between 0.00 ppm and 4.05 ppm. See [15] for a

    detailed table.

    The current results strongly support our claim that modern

    processors have relatively stable clock skews. Moreover

    we believe that if the administrators of the lab allowed

    us to exchange more packets with the 69 fingerprintees, we

    would have found the clock skews to be even more stable.

    In Section 6 we apply our clock skew estimates to a single

    computer at multiple locations and on multiple dates, and

    the skew estimates again are close (Table 3); our results below

    further support our claim of the stability of clock skews

    over time.

    6 Access technology-, topology-, and

    measurer-independent measurements

    Here we consider our experiments which suggest that

    clock skew estimates are relatively independent of the fingerprintee's

    access technology, the topology between the

    fingerprintee and the measurer, and themeasurer's machine.

    LAPTOPS IN MULTIPLE LOCATIONS. Our first set of experiments

    along these lines measures laptop connected to

    the Internet via multiple access technologies and locations

    (Table 3). For all these experiments, laptop is a Dell Latitude

    C810 notebook with a 1.133GHz Pentium III Mobile

    processor and running a default installation of Red Hat 9.0

    (Linux kernel 2.4.20-8). The measurer in all these experiLaptop

    location Start time (PDT) Duration Packets Wireless NAT Skew est.

    San Diego, CA, home cable 2004-07-09, 22:00 3 hours 181 Yes, WEP Yes ?58.17

    SD Supercomputer Center 2004-07-10, 10:00 3 hours 182 Yes No ?58.00 ppm

    CSE Dept, UCSD 2004-07-12, 12:00 3 hours 180 Yes No ?58.24 ppm

    San Diego, CA, home cable 2004-07-12, 21:00 3 hours 180 Yes Yes ?58.21 ppm

    Clinton, CT, home cable 2004-07-26, 06:00 3 hours 182 No Yes ?58.19 ppm

    San Diego, CA, home cable 2004-09-14, 21:00 30 min 1795 Yes Yes ?58.22 ppm

    SD Supercomputer Center 2004-09-22, 12:00 30 min 1765 Yes Yes ?58.13 ppm

    San Diego dialup, 33.6kbps 2004-10-18, 10:00 30 min 1749 No No ?57.57 ppm

    SD Public Library 2004-10-18, 14:45 30 min 946 Yes Yes ?57.63 ppm

    Table 3. TCP timestamps-based skew estimates of laptop running Red Hat Linux
    9.0 when connected

    to host1 from multiple locations and when not running ntpd. The traces were
    recorded at host1.

    ments, host1, is a Dell Precision 340 with a 2GHz Intel

    Pentium 4 processor located within the UCSD Computer

    Science and Engineering department and running Debian

    3.0 with a recompiled 2.4.18 Linux kernel; host1 is also

    configured to synchronize its system time with true time via


    For all experiments, we establish a TCP connection between

    laptop and host1, and then exchange TCP packets

    over that connection. On host1, we record a trace of

    the connection using tcpdump. We then use our techniques

    from Section 3 to estimate the skew of laptop's

    TSopt clock. As the horizontal line in Table 3 indicates, we

    divide our experiments into two sets. In the first set, our experiments

    last for three hours and exchange one TCP packet

    every minute (we do this by performing a sleep(60) on

    host1). For the second set of experiments, the connections

    last for 30 minutes, and a packet is exchanged at random

    intervals between 0 and 2 seconds, as determined by a

    usleep on host1. With few exceptions, the packets from

    laptop are all ACKs with no data.

    We conduct experiments when the laptop is connected

    to the Internet via residential cable networks on both coasts

    (Table 3). For our residential experiments, we use a 802.11b

    wireless connection with 128-bit WEP encryption, a standard

    (unencrypted) 802.11b wireless connection, and a

    standard 10Mbps 10baseT wired connection. We also conducted

    experiments with our laptop connected to the San

    Diego Supercomputer Center's 802.11b wireless network,

    from the UCSD Computer Science and Engineering wireless

    network, and from the San Diego Public Library'swireless

    network. As the final column in the table shows, the

    skew estimates are all within a fraction of a ppm of each

    other. (If we subsample the first set of experiments to one

    packet every 3 minutes, then the difference between the

    skew estimates for any two measurements in the first set

    is at most 0.45 ppm.)


    above results strongly suggest that skew estimates are independent

    of access technology, the above experiments do not

    stress-test the topology between the fingerprinter and the

    fingerprintee. Therefore, we conducted the following set of

    experiments. We selected a set of PlanetLab nodes from

    around the world that reported, via ntptrace, approximately

    accurate system times. We chose PlanetLab machines

    located at the University of California at San Diego,

    the University of California at Berkeley, the University of

    Washington, the University of Toronto (Canada), Princeton

    (New Jersey), the Massachusetts Institute of Technology,

    the University of Cambridge (UK), ETH (Switzerland),

    IIT (India), and Equinix (Singapore). These PlanetLab machines,

    along with host1 and (in one case) CAIDA's test

    machine with a CDMA-synchronized Dag card, served as

    our fingerprinters. Our fingerprintees were laptop and

    host1, where laptop was connected both to the SDSC

    wireless and to the CAIDA wired networks.

    For each of our experiments, and for each of our chosen

    PlanetLab nodes, we created a flow between the node

    and the fingerprintee. Over each flow our fingerprintee sent

    one packet at random intervals between 0 and 2 seconds;

    here the fingerprintee executed usleep with appropriate

    parameters. We then recorded the flows on the PlanetLab

    machines using plabdump. On host1 we recorded the

    corresponding flow using tcpdump. And on the machine

    with the Dag card we used Coral [14] (that machine was

    only reachable when laptop was connected directly to

    CAIDA's wired network). We then computed the skew using

    the techniques from Section 3. The results for laptop

    are in Table 4. Notice that the skew estimates are in general

    within a fraction of a ppm of each other, suggesting that our

    skew estimates are independent of topology.

    For distance measurements for Table 4, we used traceroute

    to determine hop count, and then used mean time between

    when tcpdump recorded a packet on the measured

    device and the time between when plabdump recorded the

    packet on themeasurer. This distance estimate also includes

    laptop, 2004-09-17, 08:00-10:00 PDT laptop, 2004-10-08, 08:00-10:00 PDT

    Measurer Skew estimate Dist. from measurer Skew estimate Dist. from measurer

    host1 ?58.23 ppm 7 hops, 2.77 ms ?58.03 ppm 8 hops, 1.16 ms

    San Diego, CA ?58.07 ppm 7 hops, 1.21 ms ?58.03 ppm 8 hops, 1.15 ms

    Berkeley, CA ?58.17 ppm 10 hops, 4.02 ms ?58.02 ppm 12 hops, 5.06 ms

    Seattle, WA ?58.15 ppm 8 hops, 14.74 ms ?58.01 ppm 9 hops, 15.12 ms

    Toronto, Canada ?58.31 ppm 16 hops, 44.43 ms

    Princeton, NJ ?57.97 ppm 13 hops, 37.59 ms ?57.91 ppm 14 hops, 36.97 ms

    Boston, MA ?57.93 ppm 12 hops, 35.82 ms ?58.10 ppm 13 hops, 41.09 ms

    Cambridge, UK ?58.06 ppm 20 hops, 84.19 ms ?58.18 ppm 21 hops, 86.45 ms

    ETH, Switzerland ?58.38 ppm 20 hops, 90.51 ms ?58.40 ppm 21 hops, 84.07 ms

    IIT, India ?59.60 ppm 16 hops, 199.27 ms

    Equinix, Singapore ?58.10 ppm 18 hops, 99.50 ms ?58.05 ppm 15 hops, 93.55 ms

    CAIDA test lab ?57.98 ppm 5 hops, 0.24 ms

    Table 4. Skew estimates of laptop, running Red Hat 9.0 with ntpd, for traces
    taken simultaneously

    at multiple locations. On 2004-09-17 the laptop was connected to the SDSC
    wireless network, and on

    2004-10-08 the laptop was connected to the CAIDA wired network. The Toronto
    and India lines have

    empty cells because the PlanetLab machines at those locations were down
    during the experiment.

    The Boston machine on 2004-10-08 was a different PlanetLab machine than the
    one on 2004-09-17.

    The empty cell for the CAIDA test lab is because the lab is only reachable
    fromCAIDA's wired network.

    the time spent in the application layers on the machines, but

    should give a rough estimate of the time it takes packets to

    go from the fingerprintee to the measurer.

    The results of these experiments suggest that our TSopt

    clock skew estimation technique is generally independent

    of the topology and distance between the fingerprinter and

    the fingerprintee. Furthermore, these results suggest that

    our skew estimation technique is independent of the actual

    fingerprinter, assuming that the fingerprinter synchronizes

    its system time with NTP [19] or something better [26].

    7 Effects of operating system, NTP, and special



    Table 5 we show skew estimates for the same physical device,

    laptop, running both Red Hat 9.0 and Windows XP

    SP2, and both with and without NTP-based system clock

    synchronization. (For this experiment, laptop sent one

    packet to the measurer, host1, at random intervals between

    0 and 2 seconds; laptop was connected to the

    SDSC wireless network, and was 7 hops away from host1;

    host1 also sent a ICMP Timestamp Request to laptop

    at random intervals between 0 and 60 seconds.) The table

    shows that, for the listed operating systems, the system

    clock and the TSopt clock effectively have the same clock

    skew when the device's system time is not synchronized

    with NTP, and that the TSopt clock skew is independent of

    whether the device's system clock is maintained via NTP.

    Although not shown in the figure, our experiments with

    OpenBSD 3.5 on another machine suggest that the TSopt

    clock and system clock on default OpenBSD 3.5 installations

    have the same skew (approximately 68 ppm). On the

    other hand, at least with this test machine, the TSopt clock

    and system clock on a default FreeBSD 5.2.1 system have

    different skews (the TSopt clock skew estimate is about the

    same as with OpenBSD, but the system clock skew estimate

    is approximately 80 ppm). When we turn on ntpd under

    FreeBSD 5.2.1, the TSopt clock skew remained unchanged.

    POWER OPTIONS FOR LAPTOPS. We also consider how

    the clock skews of devices are affected by the power options

    of laptops. In the case of Red Hat 9.0, when laptop

    is running with the power connected, if we switch to battery

    power, there is a brief jump in the TSopt clock offset-set for

    the device, and then the device continues to have the same

    (within a fraction of a ppm) clock skew. For laptop running

    Windows XP SP2, if the laptop is idle from user input

    but continues to maintain a TCP flow that we can monitor,

    then the TSopt clock skew changes after we switch to battery

    power. If we repeat this experiment several times, and

    if we boot with only battery power, we find that the clock

    skews with battery power are in all cases similar. When

    booting with outlet power, the clock skew on laptop running

    Windows XP initially begins with a large magnitude,

    and then stabilizes to a skew like that in Table 5 until we

    disconnect the power; the initially large skew may be due

    to the laptop recharging its batteries. We have not sampled

    a large enough set of laptops to determine whether the

    Start time Operating System NTP skew estimate skew estimate

    (TCP tstamps) (ICMP tstamps)

    2004-09-22, 12:00 PDT Red Hat 9.0 No ?58.20 ppm ?58.16 ppm

    2004-09-17, 08:00 PDT Red Hat 9.0 Yes ?58.16 ppm ?0.14 ppm

    2004-09-22, 21:00 PDT Windows XP SP2 No ?85.20 ppm ?85.42 ppm

    2004-09-23, 21:00 PDT Windows XP SP2 Yes ?84.54 ppm 1.69 ppm

    Table 5. Experiments for the same physical device, laptop, running different
    operating systems and

    with NTP synchronization both on and off. For all experiments, laptop was
    located on the SDSC

    wireless network. Additionally, laptop was up for an hour before the Windows

    14700 15000 15300 15600

    time since start of measurement (seconds)








    observed offset (ms)

    Figure 4. TSopt clock offset-sets for 100

    honeyd 0.8b Windows XP SP1 virtual hosts.

    Start time: 2004-09-19, 23:00PDT; honeyd

    running on host3. Points are connected in

    this figure to highlight the correlation between

    the virtual hosts.

    clock skews with battery power are a simple function of the

    clock skews with outlet power, though the skews with battery

    power seem to be consistent for a single laptop.

    8 Applications

    We now consider some applications of our techniques,

    though we emphasize that our most important results are

    the foundations we introduced in the previous sections that

    make the following applications possible.


    a honeyd [24] version 0.8b virtual honeynet consisting

    of 100 Linux 2.4.18 virtual hosts and 100 Windows XP

    SP1 virtual hosts. Our server in this experiment, host3,

    is identical to host1, has 1GB of RAM, and maintains its

    system time via NTP.We ran honeyd with standard nmap

    and xprobe2 configuration files as input; honeyd used

    the information in these files to mimic real Linux and Windows

    machines. We ran nmap and xprobe2 against the

    virtual hosts to verify that nmap and xprobe2 could not

    distinguish the virtual hosts from real machines.

    We applied our TCP timestamps- and ICMP-based skew

    estimation techniques to all 200 virtual hosts. Our fingerprinter

    in this experiment was on the same local network.

    We observed several methods for easily distinguishing

    between honeyd virtual hosts and real machines. First,

    we noticed that unlike real Linux and Windows machines,

    the virtual hosts always returned ICMP Timestamp Replies

    with zero in the transmit timestamp field. Additionally, we

    observed that the honeyd Windows XP virtual hosts had

    TSopt clocks Ctcp with Hz[Ctcp] = 2, whereas all of the real

    Windows XP machines that we tested had Hz[Ctcp] = 10.

    The lesson here is that although the nmap and xprobe2

    configuration files provide enough information for the respective

    programs to effectively fingerprint real operating

    systems, the configuration files do not provide enough information

    for honeyd to be able to correctly mimic all aspects

    of the Linux and Windows protocol stacks.

    Even if honeyd completely mimicked the network

    stacks of real Linux 2.4.18 andWindows XP SP1 machines,

    we could still use our remote physical device fingerprinting

    techniques to distinguish between our 200 virtual hosts

    and 200 real machines. Our TSopt clock skew estimates for

    all 200 virtual hosts were approximately zero and the system

    clock skew estimates for all 200 virtual hosts were approximately

    the same positive value. Given our discussion

    in Section 5 of the distribution of clock skews, this lack

    of variability in clock skews between virtual hosts is not

    what one would expect from real machines. Furthermore,

    the TSopt and system clocks between all the virtual hosts

    of the same operating system were highly correlated; e.g.,

    Figure 4 shows the TSopt offset-sets for all 100 Windows

    XP SP1 virtual hosts 241 minutes into our experiment. We

    communicated our results to the author of honeyd and, in

    response, version 1.0 of honeyd randomly assigns TSopt

    clock skews to each virtual host using a Gaussian distribution

    around the server's system time. This decision may

    affect other components of the system, e.g., if the server

    runs ntpd, changes to the server's system time may appear

    as global changes to the distribution of the virtual hosts'

    clocks. Version 1.0 of honeyd still issues ICMP Timestamp

    Replies with zero transmit timestamps. Furthermore,

    the system clocks on version 1.0 honeyd virtual hosts are

    still highly synchronized and are too fast by several orders

    of magnitude.

    To experiment with real virtualization technologies, we

    installed VMware Workstation 4.5.2 on host3, but this

    time host3 ran Red Hat 9.0. We then installed five default

    copies of Red Hat 9.0 under VMware. We applied our skew

    estimation techniques to these five virtual machines, as well

    as to host3. The results show that the five virtualmachines

    do not have constant (or near constant) clock skews, shown

    by the non-linearity of the points in Figure 5. Furthermore,

    the magnitude of the clock skews on these virtual machines

    is larger than we would expect for physical machines. We

    feel confident that these observations and natural extensions

    could prove useful in distinguishing virtual honeynets from

    real networks.


    Another natural application of our techniques is to count the

    number of devices behind a NAT. To briefly recall previous

    work in this area, Bellovin [7] showed that an adversary

    can exploit the IP ID field to count the number of devices

    behind a NAT, but his approach is limited in three ways:

    (1) the IP ID field is only 16-bits long; (2) recent operating

    systems now use constant or random IP ID fields; and (3)

    his technique cannot count the total number of devices behind

    a NAT if not all of them are active at the same time.

    Our suggested approach to this problem has two phases.

    First, partition the trace into (candidate) sets corresponding

    to different sequences of time-dependent TCP timestamps;

    creating such a partition is relatively easy to do unless two

    machines have approximately the same TSopt clock values

    at some point in time, perhaps because the machines booted

    at approximately the same time. Then apply our clock skew

    estimation techniques to each partition, counting hosts as

    unique if they have measurably different clock skews. If two

    devices have approximately the same TSopt clock values

    at some point in time but have measurably different clock

    skews, then one can detect and correct this situation in the

    analysis of the partition's offset-set.


    utility of our techniques for forensics purposes follows

    closely from our claims (1) that there is variability in the

    clock skews between different physical devices (Section 5),

    (2) that the clock skew for a single device is approximately

    constant over time (Section 5), and (3) that our clock skew

    estimates are independent of access technology, topology,

    and the measurer (Section 6). For forensics, we anticipate

    that our techniques will be most useful when arguing that a

    0 600 1200 1800 2400 3000 3600 4200 4800 5400 6000 6600 7200

    time since start of measurement (seconds)






    observed offset (seconds)

    Figure 5. TSopt clock offset-sets for five

    VMware Workstation virtual machines running

    Red Hat 9.0, and for the host, host3,

    also running Red Hat 9.0. 2004-10-27 17:00-

    19:00PDT. The top set of points corresponds

    to the TSopt clock offset set for host3.

    given device was not involved in a recorded event. With respect

    to tracking individual devices, we stress that our techniques

    do not provide unique serial numbers for devices,

    but that our skew estimates do provide valuable bits of information

    that, when combined with other sources of information

    such as operating system fingerprinting results, can

    help track individual devices on the Internet.


    for organizations that provide network traces containing

    payload data to anonymize the IP addresses in the traces

    using some prefix-preserving anonymization method [28,

    29]. If an organization makes available both anonymized

    and unanonymized traces from the same link, one can

    use our techniques to catalyze the unanonymization of the

    anonymized traces. Such a situation is not hypothetical: in

    addition to the 2004-04-28 trace that we used in Section 5,

    CAIDA took another trace from the same link on 2004-04-

    21, but the 2004-04-21 trace included payload data and was

    therefore anonymized.

    To study how one might use our clock skew estimation

    techniques to help unanonymize anonymized traces,

    on 2005-01-13 and 2005-01-21 CAIDA took two two-hour

    traces from a major OC-48 link (the same link from which

    CAIDA captured the 2004-04-28 trace). We anonymized

    the 2005-01-13 trace and experimented with our ability to

    subsequently unanonymize it. Given the value of a device's

    TSopt clock and knowledge of that clock's intended

    frequency Hz, we can compute the approximate uptime of

    the device. (Prior to our work, one method for inferring

    Hz from a passive trace would be to use a program like

    p0f [3].) As a first attempt at unanonymizing the 2005-01-

    13 trace, we paired anonymized IP addresses from 2005-

    01-13 with IP addresses from 2005-01-21 when our uptime

    estimate of a host in 2005-01-21 is eight days higher (plus

    or minus five minutes) than the uptime of a host in 2005-01-

    13 and when both hosts have the same TTLs and intended

    frequencies. Our program produced 4613 pairs of candidate

    anonymous to real mappings, of which 2660 (57.66%) were

    correct. To reduce the number of false matches, especially

    for small uptimes, we modified our program to filter out

    pairs that have TSopt clock skews different by more than 3

    ppm. We also incorporated our clock skew estimates into

    our uptime estimates. These changes reduced the number

    of candidate mappings to 2170, of which 1902 (87.65%)

    were correct. There are a total of 11862 IP addresses in

    both the 2005-01-13 and 2005-01-21 traces that have the

    TCP timestamps option enabled. Since the anonymization

    is prefix-preserving, given the candidate mappings one

    can begin to unanonymize address blocks. We are unaware

    of any previous discussion of the problems to prefixpreserving

    anonymization caused by leaking information

    about a source via the TCP timestamps option.

    9 Other measurement techniques

    Although the techniques we describe above will likely

    remain applicable to current generation systems, we suspect

    that future generation security systems might try to resist

    some of the physical device fingerprinting techniques that

    we uncover. In anticipation of these future systems, we consider

    possible avenues for clock-based physical device fingerprinting

    when information about a system's TSopt clock

    or system clock is not readily available to an adversary; we

    do not consider here but recognize the possibility of fingerprinting

    techniques that profile other aspects of a device's

    hardware, e.g., processor speed or memory. These directions

    assume that new operating systems mask or do not include

    the TSopt clock values in the TCP headers and do not

    reply to ICMP Timestamp Requests, but that the systems'

    underlying clocks still have non-negligible skews. (This assumptionmay

    not be valid if, for example, at boot a new operating

    system does amore precise estimation of the oscillator

    frequencies supplying the hardware basis for the clocks.)

    The techniques we propose in this section are less refined

    than the techniques elsewhere in this paper; we envision

    them as starting points for more sophisticated techniques.

    FOURIER TRANSFORM. Some systems send packet at 10

    or 100 ms intervals, perhaps due to interrupt processing

    or other internal operating system feature on one side of

    a flow. When this condition holds, we can use the Fourier

    transform to extract information about the system's clock

    skew. Figure 6 plots the TSopt clock offset-sets for a device

    in BBS with a 2 Hz TSopt clock. The five diagonal

    0 900 1800 2700 3600 4500 5400 6300 7200

    time since start of measurment (seconds)






    offset set (ms)

    TSopt clock skew estimate by linear programming upper bound

    Figure 6. TSopt clock skew estimate for a

    source in BBS. Trace recorded on an OC-48

    link of a U.S. Tier 1 ISP, 2004-04-28 19:30-

    21:30PDT. TSopt clock skew estimate via linear

    programming: 175.2 ppm. Clock skew estimate

    via the Fourier transform: 175.6 ppm.

    bands suggests that the machine clusters packet transmissions

    at approximately 100 ms intervals, and we can use the

    Fourier transform on packet arrival times to estimate the frequency

    at which the device actually transmits packets (here

    packet arrival times refers to the times at which the monitor

    records the packets). For the source shown in Figure 6,

    after computing the Fourier transform, the frequency with

    the highest amplitude was 25.00439, which implies a skew

    of 25.00439/25 ? 1, or 175.6 ppm. Moreover the top 19

    frequencies output by the Fourier transform all imply skews

    between 171.0 ppm and 179.3 ppm. These values are all

    close to the 175.2 ppm output by our TCP timestamps-based

    approach but do not make any use the TCP timestamps contained

    with the packets.

    Although our Fourier-based technique does not require

    knowledge of a device's TSopt or system clocks, our

    Fourier-based solution is currently not automated. This lack

    of automation, coupled with the fact that current generation

    systems readily relinquish information about their TSopt

    and system clocks, means that our Fourier-based solution

    is currently less attractive than the techniques we described

    in Sections 3 and 4.


    the system clock skew of devices that do not synchronize

    their system times with NTP, we note that many applications

    perform certain operations at semi-regular intervals.

    For example, one can configuremost mail clients to poll for

    new mail every n minutes. As another example, Broido,

    Nemeth, and claffy show that some Microsoft Windows

    2000 and XP systems access DNS servers at regular intervals

    [8]. It may be possible to infer information about a

    device's system clock skew by comparing differences between

    actual intervals of time between these periodic activities

    and what the application intends for those intervals of

    time to be.

    10 Conclusions

    In this study we verified the ability and developed techniques

    for remote physical device fingerprinting that exploit

    the fact that modern computer chips have small yet nontrivial

    and remotely detectable clock skews. We showed

    how our techniques apply to a number of different practically

    useful goals, ranging from remotely distinguishing between

    virtual honeynets and real networks to counting the

    number of hosts behind a NAT. Although the techniques we

    describedwill likely remain applicable to current generation

    systems, we suspect that future generation security systems

    might offer countermeasures to resist some of the fingerprinting

    techniques that we uncover. In anticipation of such

    developments, we discussed possible avenues for physical

    device fingerprinting when information about a system's

    TSopt clock or system clock are not readily available to the

    adversary. Our results compellingly illustrate a fundamental

    reason why securing real-world systems is so genuinely difficult:

    it is possible to extract security-relevant signals from

    data canonically considered to be noise. This aspect renders

    perfect security elusive, and even more ominously suggests

    that there remain fundamental properties of networks that

    we have yet to integrate into our security models.


    We thank Bruce Potter and Stefan Savage for helpful discussions,

    Emile Aben, Dan Andersen, Colleen Shannon,

    and Brendan White for collecting some of the traces that

    we analyzed, and William Griswold for loaning us a PDA

    from the HP Mobile Technology Solutions gift to the ActiveCampus

    project. All three authors were supported by

    the SciDAC program of the US DOE (award # DE-FC02-

    01ER25466). T. Kohno was also supported by NDSEG and

    IBM Ph.D. Fellowships.


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    johnyreb, Mar 6, 2005
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  2. johnyreb

    @ . Guest

    johnyreb wrote:
    > Remote physical device fingerprinting



    ....thanks, i needed that. but you needn't have posted more
    than the first paragraph or so.
    @ ., Mar 6, 2005
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