15th USENIX Security Symposium
Pp. 167178 of the Proceedings
Passive Data Link Layer 802.11 Wireless Device Driver
Jamie Van Randwyk5,
Motivated by the proliferation of wireless-enabled devices and the
suspect nature of device driver code, we develop a passive
fingerprinting technique that identifies the wireless device driver
running on an IEEE 802.11 compliant device. This technique is valuable to
an attacker wishing to conduct reconnaissance against a potential
target so that he may launch a driver-specific exploit.
In particular, we develop a unique fingerprinting technique that
accurately and efficiently identifies the wireless driver without
modification to or cooperation from a wireless device. We perform an
evaluation of this fingerprinting technique that shows it both quickly
and accurately fingerprints wireless device drivers in real world
wireless network conditions. Finally, we discuss ways to prevent
fingerprinting that will aid in improving the security of wireless
communication for devices that employ 802.11 networking.
Device drivers are a primary source of security holes in modern
operating systems . Drivers experience error rates
of three to seven times higher than other kernel code, making them
the poorest quality code in most kernels . There
are a large number of different device drivers available, each being a
potentially large body of code that is frequently modified to support
new hardware features. These factors and the fact that drivers are
often developed by programmers who lack intimate knowledge of the
operating system kernel contribute to the disproportionately high
number of bugs found in device drivers .
In general, device drivers execute in kernel space; hence, exploiting
a vulnerable driver leads to compromise of the entire operating
system. This threat is somewhat tempered by the fact that interacting
with a driver typically requires physical access to a system. As a
result, most security holes in device drivers are difficult to exploit
remotely. For instance, it is hard to remotely interact with, much
less exploit, a video or keyboard driver. Classes of drivers exist
with which it is possible to interact without physical access to a
system. Drivers for network devices such as wireless cards, Ethernet
cards, and modems are examples. In particular, wireless network device
drivers are easy to interact with and potentially exploit if the
attacker is within transmission range of the wireless device. Today,
the single most common and widespread wireless devices are those
conforming to the IEEE 802.11 standards . The vast
number of 802.11 devices, the ease with which one may interact with
their drivers, and the suspect nature of driver code in general has
led us to evaluate the ability of an attacker to launch a
driver-specific exploit by first fingerprinting the device driver.
Fingerprinting is a process by which a device or the software it is
running is identified by its externally observable characteristics. In
this paper, we design, implement, and evaluate a technique for
fingerprinting IEEE 802.11a/b/g wireless network drivers. Our approach
is based on statistical analysis of the rate at which common 802.11
data link layer frames are transmitted by a wireless device. Since
most wireless exploits are dependent on the specific driver being
used, wireless device driver fingerprinting can aid an attacker in
launching a driver-specific exploit against a victim whose device is
running a vulnerable driver.
Our technique is completely passive, meaning that a fingerprinter
(attacker) needs only to be able to monitor wireless traffic from the
fingerprintee (target, victim). This makes it possible for anyone within
transmission range of a wireless device to fingerprint the device's
wireless driver. Passive fingerprinting techniques have the advantage
over active approaches in that they do not transmit data, making
prevention of such techniques difficult. If an attacker can passively
determine which driver a device is using, he can successfully gain
information about his victim without fear of detection.
Our fingerprinting technique relies on the fact that most stations
actively scan for access points to connect to by periodically sending
out probe request frames. The algorithm used to scan for access points
is not explicitly defined in the 802.11 standard. Therefore, it is up
to the developers of device drivers to implement their own method for
probing. This lack of an explicit specification for a probing
algorithm in the 802.11 standard has led to the development of many
wireless device drivers that perform this function entirely
differently than other wireless device drivers. Our fingerprinting
technique takes advantage of these implementation-dependent
differences to accurately fingerprint a driver. Specifically, our
method is based on statistical analysis of the inter-frame timing of
transmitted probe requests. A timing-based approach has a number of
advantages over a content-based approach. Primary among these is the
fact that coarse-grained timing information is preserved despite the
encryption of frame content as specified by security standards such as
Wired Equivalent Privacy (WEP) or 802.11i .
Fingerprinting an 802.11 network
interface card (NIC) is not a new concept. Many tools exist, such as
Ethereal , that use
the wireless device's Media Access Control (MAC) address to identify
the card manufacturer and model number. A MAC address is an
ostensibly unique character string that identifies a specific physical
network interface. The IEEE Standards Association assigns each NIC
manufacturer a special three-byte code, referred to as an
Organizationally Unique Identifier (OUI), which identifies a
particular manufacturer. While not part of the standard, most
manufacturers use the next byte to specify the model of the NIC. There
are a few notable advantages to using our method instead of relying on
the information contained in the captured MAC address. First, the MAC
address only identifies the model and manufacturer of the NIC. Our
technique fingerprints the device driver (which resides at the
operating system level), where the bulk of exploits rest. Second,
some NICs can operate using multiple drivers, implying that the MAC
address would not be enough information to identify what driver the
NIC was using. Finally, whereas the MAC address is easily alterable
in most operating systems, the features used by our passive technique
are not a configurable option in any of the drivers tested.
Our testing demonstrates an accuracy for our method in identifying the driver
that ranges from 77-96%, depending on the network setting. Our technique
requires only a few minutes worth of network data to achieve this high level of
We also confirm that the technique can withstand realistic network conditions.
The main contributions of this paper is the design, implementation,
and evaluation of a passive wireless device driver fingerprinting
technique. Our technique is capable of passively identifying the
wireless driver used by 802.11 wireless devices without specialized
equipment and in realistic network conditions. In addition, we
demonstrate that our technique is accurate, practical, fast, and
requires little data to execute.
The remainder of the paper is organized as follows. Background
material is presented in
Section 2. Section 3 presents the design for
our wireless device fingerprinting technique. Section 4
describes the implementation of our fingerprinting technique and
Section 5 presents our experimental results and
evaluation of our technique under realistic network
conditions. Section 6 presents the limitations of our
technique and Section 7 discusses possible
ways to prevent driver fingerprinting. Finally, Section 8
examines related work and we conclude in Section 9.
2 Background: IEEE 802.11 Networks
Wireless technologies are encroaching upon the traditional realm of
"fixed" or "wired" networks. The most widely adopted wireless
networking technology thus far has been the 802.11 networking
protocol, which consists of six modulation techniques,
the most of common of which are the 802.11a, 802.11b, and 802.11g
standard amendments. The price erosion and popularity of 802.11
capable hardware (especially 802.11b/g) has made wireless networks
both affordable and easy to deploy in a number of settings, such as
offices, homes, and wireless hot spots. Because of this, 802.11 is
currently the most popular and common non-telephony communication
protocol available for wireless communication .
The 802.11 standard defines a set of protocol requirements for a
wireless MAC, or medium access control, which specifies the behavior
of data link layer communication between stations in a wireless
network. A station is simply a device with wireless capabilities, such
as a laptop or PDA with a wireless networking interface. Throughout
this paper, we often refer to stations as clients. Most 802.11
networks operate in infrastructure mode (as opposed to ad-hoc mode)
and use an access point (AP) to manage all wireless communications; it
is this type of network that is the setting for our fingerprinting
technique. An example of a simple infrastructure network with three
clients and one access point is depicted in Figure 1.
Figure 1: An infrastructure mode IEEE 802.11 network.
A key component of the 802.11 standard is the MAC specification that
outlines the function of various communication frames. The MAC
coordinates access to the wireless medium between stations and
controls transmission of user data into the air via control and
management frames. Higher-level protocol data, such as data produced
by an application, is carried in data frames.
All 802.11 MAC frames include both a type and subtype field, which are
used to distinguish between the three frame types (control,
management, and data) and various subtypes. We consider only
management frames in our passive fingerprinting technique, and
specifically focus on probe request frames. Because of this, we
only describe the most pertinent MAC frames communicated when a client
joins a wireless network, and refer the reader to the IEEE 802.11
standard specification  for a more detailed
description of MAC framing.
Each mobile client must identify and associate with an access point
before it can receive network services. In a process called active
scanning, clients use probe request frames to scan an area for
a wireless access point, providing the data rates that the client can
support inside fields of the probe request. If an access point is
compatible with the client's data rates, it sends a probe response
frame to acknowledge the request. Once a client identifies a network
and authenticates to the access point via an authentication request
and authentication response, the client can attempt to join the
network by issuing an association request. If the association is
successful, the access point will respond to the client with an
association response that includes a unique association ID for future
communications. At this point, all communication between a client and
another machine, whether it resides within the wireless network or is
located outside of it, is routed through and controlled by the access
3 Fingerprinting Approach
Our fingerprinting technique is solely concerned with the active scan
function in wireless clients. When actively scanning, clients send
probe request frames to elicit responses from access points within
transmission range. The IEEE 802.11 standard describes the active scan
function of a client as follows. For each channel, the client
broadcasts a probe request and starts a timer. If the timer reaches
MinChannelTime and the channel is idle, the client scans the
next channel. Otherwise, the client waits until the timer reaches
MaxChannelTime, processes the received probe response frames
and then scans the next channel. Further detailed specification
of the active scanning function is not provided in the IEEE 802.11
standard. As a result, implementing active scanning within wireless
drivers has become a poorly guided task. This has led to the
development of many drivers that perform probing using slightly
different techniques. By characterizing these implementation-dependent
probing algorithms, we are able to passively identify the wireless
driver employed by a device.
A number of factors affect the probing behavior of a client and make
accurate fingerprinting without client cooperation a challenging
task. From the perspective of an external fingerprinter, the probing
behavior of a client is dependent on unobservable internal factors
such as timers, and on uncontrollable external factors such as
background traffic. A robust fingerprinting method cannot rely on
client cooperation or assume a static environment, hence our technique
uses machine learning to develop a model of a driver's behavior. This
model is then used for future identification.
(a) D-Link driver for the D-Link DWL-G520 (802.11b/g) PCI wireless NIC
(b) Cisco driver for the Aironet AIR-CB21AG-A-K9 (802.11a/b/g) PCI wireless NIC
Figure 2: Plot of time delta from the previous arrival of probe request frames
transmitted by two drivers.
Having explained the intuition behind our technique, we turn our
attention to two examples of representative probing
behavior. Figure 2(a) and Figure 2(b) are plots of the
time delta between arriving probe request frames as transmitted by two
different wireless drivers. Both figures clearly depict a distinctly
unique cyclic pattern. We further describe the pertinent features of
Figure 2(b) as a way to characterize the differences between the
probing patterns. Figure 2(b) is composed of a repeating pulse
with an approximate amplitude of 50 seconds. These large pulses are
occasionally preceded and/or followed by much smaller pulses ranging
from 1-5 seconds. These pulses indicates that probing was occurring in
bursts of probe request frames sent out, on average, every 50
Upon closer inspection, one notices that the cyclic pattern exhibited
by the driver probing is characterized by small variations. Our
observations reveal there are two main reasons for this. The first
reason is due to loss caused by signal interference. A fingerprinter
could significantly reduce this type of loss by using a higher gain
antenna found on commercial grade wireless cards. The second source
of variation comes from wireless drivers continuously cycling through
all eleven channels in the 2.4 GHz ISM band in search of other access
points. The channel cycling can be considered an additional source of
loss since probe request frames transmitted on unmonitored channels
cannot be observed. Multiple wireless cards could be used to monitor
all eleven channels simultaneously; however, we make the more
realistic assumption that a fingerprinter has a single wireless card
that can only monitor a small portion (e.g. one channel at any point
in time) of the eleven channels. This loss indicates that some probe
requests are missed, and statistical approaches are needed to
compensate for the lost frames. Given the data described above, we
characterize the explicit probing behavior of a client by the sending
rate of probe request frames. In the next section, we show how to
leverage this characterization to accurately identify wireless
4 Device Driver Fingerprinting
The fingerprinting technique proceeds in two stages: trace capture and
fingerprint generation. During trace capture, a fingerprinter within
wireless transmission range of a fingerprintee captures 802.11
traffic, hereafter referred to as the trace. During fingerprint
generation, the captured trace is analyzed using a supervised Bayesian
approach to generate a robust device driver fingerprint.
4.1 Trace Capture
To begin the trace capture phase, we first consider how a
fingerprinter might obtain a trace of probe request frames from a
wireless device using widely available hardware and software. We
assume a one-to-one mapping of MAC addresses to wireless devices, and
believe this to be a reasonable assumption. Because each wireless NIC
is assigned a unique MAC address by its manufacturer, the only cause
for duplicate MACs on a network would be the result of a user
reassigning his MAC address independently. However, as there are
theoretically 248 acceptable MAC addresses, the probability of a
user choosing an existing MAC on the network is negligible7. In Section 7,
we address the effects that violating this assumption has on our
The fingerprinter can use any device that is capable of eavesdropping
on the wireless frames transmitted by the fingerprintee. Therefore,
the fingerprinter must be within receiving range of the
fingerprintee's wireless transmissions. We assume the fingerprinter is
using a single, high-gain, COTS (commercial off-the-shelf) wireless
card. Next, the fingerprinter must configure their wireless card to
operate in monitor mode; this mode allows the wireless card to capture
frames promiscuously (e.g. whether they are specifically addressed to
that wireless card or not). The fingerprinter must prevent their card
from associating with an access point or sending its own probe request
frames so collection is completely passive. This allows the
fingerprinter to capture all frames sent on the current channel,
including probe request frames, without interfering with the network's
normal operation. We assume that the fingerprinter's machine is
running an OS and driver combination that supports a wireless card in
monitor mode. This can be easily done in Linux, FreeBSD, and Mac OS X.
Finally, the fingerprinter can use a network protocol analyzer, such
as Ethereal , to record the eavesdropped frames and
filter out all irrelevant data. After following the above steps, the
fingerprinter should have sufficient data to construct graphs similar
to Figures 2(a) and 2(b).
4.2 Fingerprint Generation
After a trace has been captured, the data must be analyzed to
characterize the probe request behavior. Previous work has shown that
a simple supervised Bayesian approach is extremely accurate for many
classification problems . We chose to employ
a binning approach to characterize the time deltas between probe
requests because of the inherently noisy data due to frame loss.
Binning works by translating an interval of continuous data points
into discrete bins. A bin is an internal value used in place of the
true value of an attribute. The binning method smooths probabilities
for the continuous attribute values by placing them into
groups. Although binning causes some loss of information for
continuous data, it allows for smooth probability estimates. Some
noise is averaged out because each bin probability is an estimate for
that interval, not individual continuous values. We chose to use
equal-width binning where each bin represents an interval of the same
size. While more sophisticated schemes may be available, this simple
approach generated distinct fingerprints of probe inter-arrival times
and provided a successful means for driver identification.
After performing a number of data analysis tests, we isolated two
attributes from the probing rate that were essential to fingerprinting
the wireless driver. The first attribute was the bin frequency of
delta arrival time values between probe request frames. The second
attribute was the average, for each bin, of all actual (non-rounded)
delta arrival time values of the probe request frames placed in that
bin. The first attribute characterizes the size of each bin and the
second attribute characterizes the actual mean of each bin. Our next
step was to create a signature (Bayesian model) for each individual
wireless driver that embodies these attributes. Building models from
tagged data sets is a common technique used in supervised Bayesian
|Bin ||Percentage ||Mean |
|0 ||0.676 ||0.16 |
|1.2 ||0.228 ||1.72 |
|50 ||0.096 ||49.80 |
Table 1: Sample signature for the Cisco Aironet 802.11 a/b/g PCI driver
We now describe the process used to transform raw trace data into a device
signature. To calculate the bin probabilities, we rounded the actual delta
arrival time value to the closest discrete bin value. For example, if the bins
were of a fixed width of size 1 second, any probe request frames with a delta
arrival value in (0, 0.50] seconds would be placed in the 0 second bin,
any probe request frames with a delta arrival value in (0.51, 1.50]
seconds would be placed in the 1 second bin, and so forth. Based on empirical
optimization experiments presented in our results section, we use an optimal bin
width size of 0.8 seconds. The percentage of the total probe request frames
placed in each bin is recorded along with the average, for each bin, of all
actual (non-rounded) delta arrival time values of the probe request frames
placed in that bin. These values comprise the signature for a wireless driver
which we add to a master signature database containing all the tagged signatures
that are created. An example of a signature created from the probe request
frames in Figure 2(b) is shown in Table 1. New signatures can
be inserted, modified, or deleted from the database without affecting other
signatures. This allows collaborative signature sharing, similar to how Snort
 intrusion detection signatures are currently shared.
Once the master signature database is created, a method is required to
compute how "close" an untagged signature from a probe request trace
is to each of the signatures in the master signature database.
4.3 Calculating Closeness
Let us now assume that an attacker has obtained a trace and created a
signature T of the probe request frames sent from the
fingerprintee. Let pn be the percentage of probe request frames in
the nth bin of T and let mn be the mean of all probe
request frames in the nth bin. Let S be the set of all signatures
in the master signature database and let s be a single signature
within the set S. Let vn be the percentage of probe request
frames in the nth bin of s and let wn be the mean of all probe
request frames in the nth bin of s. The following equation was
used to calculate the distance between the observed, untagged
fingerprintee signature, T, and all known master signatures,
assigning to C the distance value of the closest signature in the
master database to T:
Our technique iterates through all bins in T,
summing the difference of the percentages and mean differences scaled
by the percentage. The mean differences are scaled by the s bin
percentage to prevent this value from dominating the bin percentage
differences. We show in our results that the features included in a
signature and our final method of calculating signature difference are
effective in successfully fingerprinting wireless device drivers.
We tested our fingerprinting technique with a total of 17 different
wireless interface drivers in their default configurations. We
characterized wireless device drivers for the Linux 2.6 kernel,
Windows XP Service Pack 1 and Service Pack 2, and Mac OS X 10.3.5. The
machine we used to fingerprint other hosts' wireless drivers was a 2.4
GHz Pentium 4 desktop with a Cisco Aironet a/b/g PCI wireless card,
running the Linux 2.6 kernel and the MadWifi wireless NIC driver
. Various Pentium III class desktop machines and one
Apple PowerBook laptop were used as fingerprintee machines.
We address five primary characteristics that we expect any
fingerprinting technique to be evaluated against. First, we
investigate the resolution of our method. Specifically, we evaluate
our identification granularity between drivers for different NICs,
different drivers that support identical NICs, and different versions
of the same driver. Second, we evaluate the consistency of our
technique. We measure how successful our fingerprinting technique is
in a variety of scenarios and over multiple network sessions, after
operating system reboot, and when using the same driver to control
different NICs. Third, we test the robustness of our technique. We
conduct our experimentation in realistic network settings that
experience loss rates similar to other wireless infrastructure
networks. Fourth, we analyze the efficiency of our technique with
respect to both data and time. Finally, we evaluate the resistance of
our technique to varying configuration settings of a driver and
evaluate the potential ways one might evade our fingerprinting
To address these issues, we conducted a number of experiments using
different wireless drivers and cards across a number of different
operating system environments. In all cases, our technique
successfully fingerprinted the wireless driver in at least one
configuration. While the amount of time needed to collect the data
varied across drivers and configurations, we required only a small
amount of captured wireless traffic to fingerprint drivers accurately.
From our initial observations, we identified two properties of a
device and driver that altered their signatures. The first property
concerned whether the wireless device was unassociated or associated
to an access point. Our initial experiments revealed that, by default,
all wireless drivers transmit probe request frames when disassociated
from an access point. Additionally, many continue to send probe
requests even after association to an access point, though often not
as frequently. The second property (only applicable to Windows
drivers) concerns how the driver is managed. For many drivers, the
Windows operating system can manage the configuration of the network
settings for the wireless device instead of having a standalone
(vendor provided) program perform those functions. The standalone
program is provided by the manufacturer of the wireless device and
often supports more configuration options for the specific driver,
though also requires more user interaction to manage the device. We
noticed slight differences in the behavior of probing depending on
which option a user chose to manage their device. Due to these
differences, we treated each of these property scenarios uniquely and
created signatures to identify a driver under any of the appropriate
(a) Test set 1 and master signature experimental setup.
(b) Test set 2 experimental setup.
(c) Test set 3 experimental setup.
|Figure 3: Our test scenarios. R is the fingerprinter.
5.1 Building the Master Signatures
We collected trace data and constructed individual signatures with the
same structure as the example signature in Table 1. This was repeated
for all 17 wireless drivers in every configuration known to affect the
signature and supported by the wireless driver. Drivers from Apple,
Cisco, D-Link, Intel, Linksys, MadWifi (for Atheros chipset-based
cards running under Linux), Netgear, Proxim, and SMC were included in
our testing. A majority of the drivers included in our tests were for
Windows; therefore most of the drivers initially had four individual
signatures. We will refer to the four different configurations as
follows: (1) unassociated and controlled by Windows, (2) unassociated
and controlled by a standalone program, (3) associated and controlled
by Windows, (4) associated and controlled by a standalone program.
Three drivers did not support networking control by Windows (options 1
and 3), and four of the drivers tested did not transmit probe request
frames when associated. This meant that initially, 57 signatures were
compiled in the master signature database. We collected four
signatures at a time and each signature trace contained a minimum of
12 hours worth of data points. A 30 minute portion of each trace was
set aside and not used in signature training. This data was used as
test set 1, which we further describe in the next section. As can be
seen from Figure 3(a), the observing machine's antenna was
placed approximately 15 feet from the fingerprintee machines, and no
physical obstructions were present between the machines. Also, no
802.11 wireless traffic was detected besides the traffic generated by
After analyzing these signatures, we noted that changing configurations for some
drivers had little impact on the probe request frame transmission rate and
consequently, the generated signatures were indistinguishable from one another.
We considered these signatures to be duplicates and removed all but one from the
master signature database. This process could be automated by eliminating
signatures that are insufficiently different from others with respect to some
similarity threshold. There was only a single case where two of the drivers
from the same manufacturer (Linksys) had indistinguishable signatures. For this
case, we again left only a single signature in the master signature database.
After pruning the database of all duplicate signatures, there remained 31 unique
signatures. Each signature was tagged with the corresponding driver('s) name and
configuration(s). The entire master signature database is included as Appendix
5.2 Collecting Test Data
We used the unused 30 minute trace from each of the 57 raw signature
traces collected during master signature generation as test set
1. This scenario verifies that our signature generation adequately
captures the probing behavior of the driver and that signatures can
identify their associated drivers with a limited amount of traffic.
To demonstrate that our technique is repeatable and still accurate in
conditions other than where the signature data was originally
collected, we repeated the 57 half hour experiments in two different
physical locations. Using multiple environments helps to validate the
consistency and robustness of our technique and suggests that it works
well outside of lab settings. The arrangement for test set 2, as shown
in Figure 3(b), was as follows: we placed the
fingerprinter's antenna 25 feet from the fingerprintees with one
uninsulated drywall placed in between the machines. As in
Figure 3(a), no 802.11 wireless traffic was detected
besides that generated by the fingerprintees. For test set 3, depicted
in Figure 3(c), the observer's antenna was placed ten feet
from the fingerprintees with two desks and other miscellaneous objects
physically located between the machines. At this location, four to
twelve other wireless devices were communicating during our data
collection. Test set 2 might represent a wireless network in a
semi-isolated setting, such as a hotel room with wireless access.
Test set 3, on the other hand, represents a more congested wireless
network, such as a network located in a coffee shop or airport.
|Test Set ||Successful ||Total ||Accuracy |
|1 ||55 ||57 ||96% |
|2 ||48 ||57 ||84% |
|3 ||44 ||57 ||77% |
Table 2: Accuracy of fingerprinting technique by scenario.
5.3 Fingerprinting Accuracy
The accuracy of our technique in correctly identifying the wireless
driver operating a NIC for the three test scenarios is shown in Table
2. These results use the full half hour of data points.
Later in this section, we will explore the effects of using less data
points on the accuracy of our technique. The results also differed
based on location. As expected, our technique is the most accurate for
test set 1 (originally taken from the large signature traces) at
96%. The second most accurate test set was test set 2 (with only a
single wall and no other 802.11 traffic) at 84%, and the last
location had a 77% identification accuracy. These results indicate
that different environments affect the accuracy of our technique.
However, our technique remains reliable in all the the environments in
which we tested.
Figure 4: Number of individual drivers achieving an interval of
accuracy over all test sets.
Figure 4 demonstrates that our technique is perfectly
accurate at fingerprinting nine of the wireless drivers and over 60%
successful at identifying the other eight drivers. The accuracy of our
method at identifying a particular driver is largely dependent on how
dissimilar the driver's signature(s) are from other signatures in the
master signature database. If the correct signature is similar to
another in the database, noise such as background traffic may lead to
our technique incorrectly fingerprinting a wireless driver. These
results show that the majority of wireless drivers do have a distinct
signature. It is important to note that even with drivers that have
less unique fingerprints, we still correctly identify the driver for a
majority of the test cases.
It is also relevant to note that in cases where the technique cannot
uniquely identify a driver, it was able to narrow the possibilities
down to those drivers that have similar signatures. Though not
supported in the current implementation of our technique, it is
conceivable to list the signatures in the master signature database
that are close to the unidentified observed signature.
5.4 Empirical Bin Width Tuning
The bin width for signatures was empirically optimized during our
experimentation on test set 1 by varying the size in testing and
selecting an optimal width based on fingerprinting accuracy. This
optimization began by starting with a bin width of 0.1 seconds and
incrementally increasing the bin width by 0.1 seconds up to a bin
width of 5.0 seconds. Figure 5 reveals that a bin
width of 0.8 seconds produced the highest accuracy (96%) in test set
1, and thus, was the bin width used for the rest of our experiments.
Figure 5: Empirical bin width tuning. Shows that 0.8 second wide bins generate the
highest accuracy (96%) for test set 1.
5.5 Time Required to Fingerprint Driver
To address our technique's efficiency, we investigated the data and
time thresholds required to accurately fingerprint a driver. Ideally,
a fingerprinter would be able to identify a wireless driver in real
time after only a small traffic trace. We measured the fingerprinting
accuracy of our method in each test scenario with one minute of
collected data and increased the amount of data in one minute
increments until the full thirty minute trace from each setting was
used. Figure 6 illustrates the accuracy of our
technique in each of the three test cases corresponding to the amount
of trace data used for fingerprinting.
Figure 6: Effects of trace duration on fingerprinting accuracy.
Since the rate of probe request frames is different for most wireless drivers,
it is difficult to estimate how many probe request frames will be recorded
during one minute of observation, though for statistical interest, the average
number of probes detected during one minute of observation was 10.79 across all
of our testing scenarios. The accuracy of our technique is at least 60% in each
of the three test cases after only one minute of traffic. These results
show that our method successfully converges relatively fast on the correct
wireless driver and needs only a small amount of communication traffic to do so.
In the course of our evaluation, we discovered a few limitations of
our fingerprinting technique. We discuss these in detail below.
6.1 Driver Versions
One of the original questions we posed concerned the resolution of our
technique. We have shown that our technique is capable of
distinguishing between different drivers the vast majority of the
time. We are also interested in whether our method can distinguish
between two different versions of the same wireless driver. A number
of wireless card manufactures have released new versions of their
wireless drivers to support new features. We tested our fingerprinting
technique on six wireless drivers, with multiple driver versions
available to determine if it was possible to distinguish between
different versions of the same wireless driver. Our technique was
unsuccessful in distinguishing between different versions of the same
driver. This is a limitation of our fingerprinting technique since a
new version of a driver might patch previous security vulnerabilities
in the driver. However, even without the ability to distinguish
between versions, our fingerprints greatly reduce the number of
potential wireless drivers that a target system is running.
6.2 Hardware Abstraction Layer
Another unexpected limitation was found when testing the MadWifi
driver for Linux. This driver works with most wireless cards
containing the Atheros chipset because of the inclusion of a Hardware
Abstraction Layer (HAL). This creates a more homogeneous driver
environment since a majority of wireless cards currently available use
the Atheros chipset. The side effect is that the lack of driver
diversity reduces the appeal of fingerprinting wireless
drivers. However, one drawback of a single (or relatively small number
of) hardware abstraction layer(s) is that it magnifies any security
7 Preventing Fingerprinting
Several methods can be used to prevent our technique from successfully
fingerprinting drivers. These methods include configurable probing,
standardization, automatic generation of noise, driver code
modification, MAC address masquerading, and driver vulnerability
7.1 Configurable Probing
One solution to prevent our fingerprinting technique is for device
drivers to provide the option to explicitly disable or enable probe
request frames. It makes sense for this to be a configurable option
not only to prevent fingerprinting but also to conserve power and
bandwidth. Probe request frames are used to find networks matching the
available data rates on the client device . The SSID of
the desired network can be specified or can be set to the broadcast
SSID when probing for any available networks. By default, access
points transmit beacon frames, which announce the access point's
presence and some configuration information8. Thus, passively listening for
beacons (i.e., turning off probe request frames) could be an effective
method of discovering access points. Another solution would be to
configure wireless device drivers, by default, to passively listen for
beacons and only send probe requests for available networks when
manually triggered by the user.
An effective, but potentially difficult to implement solution for
preventing driver fingerprinting is to specify the rate at which probe
request frames are transmitted in a future IEEE standard for the
802.11 MAC. Another step towards standardization could result if a
corporate body or open source consortium was formed to develop a
standard agreed upon by all driver manufactures. If all driver
manufactures adhered to such a standard, the described fingerprinting
method would be rendered useless. Unfortunately, there are many
obstacles preventing such a standard, the major factor being that some
device manufacturers will not want to design devices that expend the
power or bandwidth necessary to transmit probe requests at a standard
rate. Due to this reason alone, it is doubtful that there will be any
standardization agreed upon and followed by every driver manufacture
concerning the rate of probe request frame transmission.
7.3 Automated Noise
Another strategy to prevent wireless driver fingerprinting is to
generate noise in the form of cover probe request frames. Cover
traffic disguises a driver by masking the driver's true rate of probe
request transmission. Due to the fact that our technique uses
statistical methods to filter out noise, the cover traffic would need
to be sufficiently random and transmit enough cover to confuse our
technique. A limitation of this approach is that the cover probe
request frames waste bandwidth the device would otherwise use for
wireless traffic, and for devices with limited power supplies,
transmitting cover traffic would reduce battery life
significantly. Also, given enough observation data, the fingerprinter
might be able to filter away the noise and successfully fingerprint
the driver. Generating noise is a difficult problem as many data
mining algorithms have been shown to be effective in filtering out
such noise and recovering the original
7.4 Driver Code Modification
For open source drivers such as the Madwifi drivers, the driver code
could be modified to change the transmission rate of probe request
frames. This alteration would fool our fingerprinting
technique. However, this is only possible for open source drivers and
would require a skilled programmer to alter the driver code. This
would not be possible for many windows drivers, since most do not
provide source code.
7.5 MAC Address Masquerading
Earlier, we made the assumption of a one-to-one mapping of MAC
addresses to wireless devices. One method to prevent driver
fingerprinting is to change the device's MAC address to match the MAC
address of another device within transmission range. This would fool
our fingerprinting technique into believing probe requests from two
different wireless drivers are originating from the same wireless
driver. There are a number of problems with this solution. First, the
wireless device must make certain that the fingerprinter is within
transmission range of both wireless devices. If the fingerprinter only
observes probe request frames from one of the two devices, it will not
be deceived. Also, since our method uses statistical methods to filter
noise, the wireless device needs to make certain that the other device
is transmitting enough probe request frames to mask its signature.
7.6 Driver Patching
While driver patching is not a full solution, we feel the creation of
well thought out driver patching schemes would improve the overall
security of device drivers as new driver exploits are found. Current
research is being conducted to improve the process of patching
security vulnerabilities [15,16]. The device driver
community should leverage this research to create more robust patching
methods, and improve the overall level of driver security.
8 Related Work
Various techniques for system and device level fingerprinting have
been used for both legitimate uses, such as forensics and intrusion
detection, as well as malicious uses, such as attack reconnaissance
and user profiling. The most common techniques take advantage of
explicit content differences between system and application
responses. Nmap , p0f , and
Xprobe  are all open source, widely distributed tools
that can remotely fingerprint an operating system by identifying
unique responses from the TCP/IP networking stack. As the TCP/IP stack
is tightly coupled to the operating system kernel, these tools match
the content of machine responses to a database of OS specific response
signatures. Nmap and Xprobe actively query the target system to
invoke these potentially identifying responses. In addition to this
active probing, p0f can passively fingerprint an operating system by
monitoring network traffic from a target machine to some third party
and matching characteristics of that traffic to a signature database.
Data link layer content matching can also be used to identify wireless
LAN discovery applications , which can be useful for
wireless intrusion detection.
While datagram content identification methods are arguably the most
simple, they are also limited to situations where datagram
characteristics are uniquely identifiable across systems, as well as
accessible to an outside party. Except for a few unique instances,
802.11 MAC-layer frame formatting and content is generally
indistinguishable across wireless devices; because of this, more
sophisticated methods are often
required. In , the authors present a
technique to identify network devices based on their unique analog
signal characteristics. This fingerprinting technique is based on the
premise that subtle differences in manufacturing and hardware
components create unique signaling characteristics in digital
devices. While the results of analog signal fingerprinting are
significant, this method requires expensive hardware such as an analog
to digital converter, IEEE 488 interface card, and digital sampling
oscilloscope. Also, it is not clear from their analysis of wired
Ethernet devices whether this method would be feasible in a typical
wireless network setting where noise from both the environment and
other devices is a more pressing consideration.
A device's clock skew is also a target for fingerprinting. A technique presented
in  uses slight drifts in a device's TCP option clock to identify
a network device over the Internet via its unique clock skew. Whereas our
technique fingerprints which driver a wireless device is running, time skew
fingerprinting is used to identify distinct devices on the Internet. Concerning
security, unique device fingerprinting is often not as useful as driver and
other types of software fingerprinting. As opposed to content based fingerprinting, both
analog signal and time skew fingerprinting exploit characteristics of the
underlying system hardware, making these techniques much more difficult to
Identification via statistical timing analysis in the context of
communication patterns and data content has been especially studied in
the area of privacy enhancing technologies. While network security
mechanisms such as encryption are often utilized to protect user
privacy, traffic analysis of encrypted traffic has proven successful
in linking communication initiators and recipients participating in
systems [23,24]. Traffic analysis has
also been applied to Web page fingerprinting. In ,
the authors demonstrate a technique that characterizes the
inter-arrival time and datagram sizes of web requests for certain
popular web sites. Using these web page characterizations, one can
identify which sites users on wireless LANs are visiting despite these
users browsing the Internet via encrypted HTTP traffic streams.
The techniques described above serve as only a survey of existing
fingerprinting techniques for systems, devices, and even static
content. The approaches vary from exploiting content anomalies in the
TCP/IP stack to characterizing time-based system behavior at both the
physical and software layers of a system. While the approaches vary,
these contributions bring to light the true feasibility of
fingerprinting via avenues otherwise assumed to be uniformly
implemented across systems.
We designed, implemented, and evaluated a technique for passive
wireless device driver fingerprinting that exploits the fact that most
IEEE 802.11a/b/g wireless drivers have implemented different active
scanning algorithms. We evaluated our technique and demonstrated that
it is capable of accurately identifying the wireless driver used by
802.11 wireless devices without specialized equipment and in realistic
Through an extensive evaluation including 17 wireless drivers, we
demonstrated that our method is effective in fingerprinting a wide
variety of wireless drivers currently on the market. Finally, we
discussed ways to prevent fingerprinting that we hope will aid in
improving the security of wireless communication for devices that
employ 802.11 networking.
Some of this work was performed while the authors were at Sandia National
Laboratories - California. Sandia is a multiprogram laboratory operated by
Sandia Corporation, a Lockheed Martin Company, for the United States Department
of Energy's National Nuclear Security Administration under Contract
DE-AC04-94AL85000. The authors greatly appreciate the staff of Sandia National
Laboratories for their assistance. The authors would like to thank John
Bethencourt, Nikita Borisov, Frank Hemingway, Adam Lee, Kristen Pelon, Amanda
Stephano, and the anonymous reviewers for their useful suggestions. This work
was partially supported by NSF Grant ITR-0428887 (Spectrum Management Toward
Spectrum Plenty) and the University of Colorado. Jason Franklin performed this
research while on appointment as a U.S. Department of Homeland Security (DHS)
Fellow. The views expressed in this paper do not necessarily reflect the
policies and views of DHS, DOE, or affiliated organizations.
Ken Ashcraft and Dawson R. Engler.
Using Programmer-Written Compiler Extensions to Catch Security
In Proceedings of IEEE Symposium on Security and Privacy, May
Andy Chou, Junfeng Yang, Benjamin Chelf, Seth Hallem, and Dawson R. Engler.
An Empirical Study of Operating System Errors.
In Proceedings of Symposium on Operating Systems Principles
(SOSP 2001), October 2001.
Tal Garfinkel, Ben Pfaff, Jim Chow, Mendel Rosenblum, and Dan Boneh.
Terra: A Virtual Machine-Based Platform for Trusted Computing.
In Proceedings of Symposium on Operating Systems Principles
(SOSP 2003), October 2003.
IEEE-SA Standards Board.
IEEE Std IEEE 802.11-1999 Information Technology - Wireless LAN
Medium Access Control (MAC) And Physical Layer (PHY) Specifications.
IEEE Computer Society, 1999.
IEEE-SA Standards Board.
Amendment 6: Medium Access Control (MAC) Security Enhancements.
IEEE Computer Society, April 2004.
Ethereal: A network protocol analyzer.
Web site, 2006.
Matthew S. Gast.
802.11 Wireless Networks: The Definitive Guide.
O'Reilly & Associates, Inc., Sebastopol, CA, USA, 2nd edition, 2005.
Nir Friedman, Dan Geiger, and Moises Goldszmidt.
Bayesian Network Classifiers.
Machine Learning, 29(2-3):131-163, 1997.
T. Hastie, R. Tibshirani, and J. H. Friedman.
The Elements of Statistical Learning.
Snort Intrusion Detection and Prevention system.
Web site, 2006.
Madwifi: Atheros chip set drivers.
Web site, 2006.
D. Agrawal and C. C. Aggarwal.
On the Design and Quantification of Privacy Preserving Data Mining
In Proceedings of Symposium on Principles of Database Systems,
R. Agrawal and R. Srikant.
Privacy-preserving data mining.
In Proceedings of ACM SIGMOD, May 2000.
B. Hoh and M. Gruteser.
Location Privacy Through Path Confusion.
In Proceedings of IEEE/CreateNet International Conference on
Security and Privacy for Emerging Areas in Communication Networks (SecureComm
Gautam Altekar, Ilya Bagrak, Paul Burstein, and Andrew Schultz.
OPUS: Online Patches and Updates for Security.
In Proceedings of 14th USENIX Security Symposium, Aug 2005.
John Dunagan, Roussi Roussev, Brad Daniels, Aaron Johnson, Chad Verbowski, and
Towards a Self-Managing Software Patching Process Using Black-Box
In First International Conference on Autonomic Computing
Nmap: a free network mapping and security scanning tool.
Web site, 2006.
Project details for p0f.
Web site, 2004.
Arkin and Yarochkin.
Xprobe project page.
Web site, August 2002.
Layer 2 Analysis of WLAN Discovery Applications for Intrusion
Web site, 2002.
Ryan Gerdes, Thomas Daniels, Mani Mina, and Steve Russell.
Device Identification via Analog Signal Fingerprinting: A Matched
In Proceedings of the Network and Distributed System Security
Symposium Conference (NDSS 2006), 2006.
Tadayoshi Kohno, Andre Broido, and K. C. Claffy.
Remote Physical Device Fingerprinting.
In Proceedings of the 2005 IEEE Symposium on Security and
Privacy (SP 2005), Washington, DC, USA, 2005.
Traffic Analysis: Protocols, Attacks, Design Issues, and Open
In Proceedings of Privacy Enhancing Technologies Workshop (PET
2000), May 2000.
Mathewson and Dingledine.
Practical Traffic Analysis: Extending and Resisting Statistical
In Proceedings of Privacy Enhancing Technologies Workshop (PET
2004), May 2004.
George Dean Bissias, Marc Liberatore, and Brian Neil Levine.
Privacy Vulnerabilities in Encrypted HTTP Streams.
In Proceedings of Privacy Enhancing Technologies Workshop (PET
2005), May 2005.
Web site, 2006.
This appendix includes the entire master signature database from our
evaluation section. It is organized with the name of the wireless
driver, if the driver was associated (assoc) or unassociated (unassoc),
and if Windows (win) was configuring the wireless device, or a
standalone program (native). The values after the driver name and
configuration are a set of tuples ordered as follows: (Bin Value,
Percentage, Bin Mean Value).
1Carnegie Mellon University,email@example.com
2University of Colorado, Boulder, firstname.lastname@example.org
3University of Illinois, Urbana-Champaign, email@example.com
4University of California, Davis,firstname.lastname@example.org
5Sandia National Laboratories,email@example.com
6University of Colorado, Boulder,firstname.lastname@example.org
is important to note that some attackers will sniff the MAC addresses
of other users on a wireless network to use as their own, giving them
the ability to steal a connection or hide their malicious
actions. Although we acknowledge that this scenario would bring about
duplicate MAC addresses on a network, we believe it is far from the
common case in most network settings.
8This is in
contrast to disabling the SSID broadcast function. Disabling SSID
broadcast simply forces an AP to send a string of spaces or a null
string in the SSID field of the beacon frame. Kismet 
reports this SSID as <no ssid>.