Physically Locating Wireless Intruders
(ATC-NY, New York, USA
(ATC-NY, New York, USA
(ATC-NY, New York, USA
Golden G. Richard III
(University of New Orleans, Louisiana, USA
Abstract: Wireless networks, specifically IEEE 802.11, are inexpensive
and easy to deploy, but their signals can be detected by eavesdroppers
at great distances. Even with existing and new security measures, wireless
networks have a higher risk than wired nets. WIDS, Wireless Intrusion Detection
System, provides an additional layer of security by combining intrusion
detection with physical location determination, using directional antennas.
We briefly describe WIDS and present our initial results of remote station
location using inexpensive hardware.
Key Words: Wireless, intrusion detection system, 802.11, antenna
Wireless local area networks (WLANs) are very popular due to their availability
and low price. Portable device manufacturers are already providing 802.11
wireless cards as a standard built-in networking device. Installing a base
station and a wireless card in one or more devices (PC, laptop, printer,
etc) gives an almost instant mobile network, which can use a high-speed,
typically broadband, Internet connection. This is faster, cheaper and much
more convenient than running CAT-5 cable and installing outlets, hubs and
switches for a traditional Ethernet network.
The wireless medium by its very nature cannot be contained. Reliable
omni-directional communication for devices at 100m (the typical range of
an 802.11 Access Point (AP)) requires a signal strength that is easily
detected at greater distances. Craig Ellison's [Ellison
2001] research showed that a majority of 802.11b wireless LANs are
vulnerable. Using a laptop with a wireless card and a 14db Yagi antenna
mounted on a tripod, he quickly identified 61 APs within a six-block radius
in Manhattan. The shareware program NetStumbler reports detailed information
about each AP.
This technique, called "war-driving," is increasingly popular,
and there are sites dedicated to mapping unprotected wireless networks
(e.g., http://netstumbler.com and
The industry approach has been to layer data encryption onto the wireless
signal with first 40 bit and then 128 bit encoding. The 802.11 standard
specifies Wired Equivalent Privacy (WEP), a link-layer security protocol.
WEP is based on the RC4 stream cipher, a symmetric cipher (the same key
is used for both encryption and decryption). These security mechanisms-intended
to maintain the confidentiality, integrity, and availability of wireless
communications-are problematic. Several WEP flaws have been widely documented
and disseminated [see Hayes 2001], [Information
Security 2001], and [http://www.cs.umd.edu/~waa/wireless.html].
Each of these flaws allows passive or active attacks on wireless transmissions,
by which attackers can decrypt information or inject forged information
into the transmissions.
Several vendors, such as 3Com, Cisco, DLink, LinkSys, added access control
lists (ACLs), implemented through MAC address filtering, to increase security.
MAC address filtering amounts to allowing predetermined clients with specific
hardware addresses to authenticate and associate. Unfortunately, MAC addresses
can be forged and MAC address filtering is not available for ad-hoc (i.e.,
peer-to-peer) 802.11 networks.
The 802.11i protocol addresses most of WEP's shortcomings; however,
several problems remain. First, a large installed base of legacy systems
will remain unprotected for some time. Second, flaws will always exist,
due to misconfiguration and implementation bugs. And third, authentication
mechanisms can be compromised by lost or stolen equipment.
Because of these ever-present risks, a layered protection mechanism
is needed. WIDS, Wireless Intrusion Detection System, can provide an extra
layer of protection that detects intruders. In addition, it determines
the physical location of intruders, information not provided by any other
means. The WIDS approach allows the presence and location of the
intruder to be determined. This paper focuses on the experimental results
obtained from our initial work on developing an early WIDS prototype.
The rest of the paper is organized as follows. Section
2 presents the problem. Section 3 describes the
WIDS approach. Section 4 outlines the experiments we
performed on directional location of intruders. We present the results
of the experiments in Section 5 and conclusions and
future work in Section 6.
2 Problem Statement
The combination of inherent insecurity of wireless networks (signals
radiating further than the intended coverage area) and weaknesses in the
current security mechanisms make them open targets for attacks, which limits
their deployment. We focus on active attacks. Because an intruder can attack
from any point close enough to an Access Point, it is a challenge to devise
an effective intrusion detection system. Knowing the physical location
of the attacker aids in the intrusion detection, as well as the response.
Although the industry has been betting that the benefits of wireless
technology outweigh the security risks, some customers, such as the military,
have no alternative to very selective deployment of wireless networks and
severe limits on the data they carry. Current intrusion detection mechanisms
are not flexible enough to provide early detection of intruders in wireless
networks. WIDS improves upon the state-of-the-art by providing earlier
detection capabilities than are currently available.
3 WIDS Approach
WIDS comprises three tasks. First, a WIDS access point must detect a
signal from a remote station. Second, based on the data in the signal (MAC
address, IP header information, application data, etc.), the AP determines
the remote station is an intruder. And finally, two or more APs determine
the location of the intruder, using directional antennas. We describe experiments
to test the location capability later in this paper.
An intrusion detection system (IDS) coordinates the physical location
data, events seen on the network, and preset administrative policies.
Related work in WLAN intrusion detection has been done by Wright and
Foust. Wright [Wright 2002] proposed techniques to
detect war-driving programs, including NetStumbler, but focused on probe
detection only, not physical location. Foust [Foust
2002] proposed a simple method of locating remote stations using signal
strength but used only fixed omnidirectional antennas.
In the following sections, we describe the WIDS APs, the directional
antennas, and the IDS architecture.
3.1 WIDS AP
The typical WIDS installation [see Fig. 1], consists
of a normal omni-directional AP located in the center of the physical facility
and WIDS APs located around the perimeter and directional antennas pointing
outward. (Note that there could be multiple omni, or directional, APs inside
the perimeter; we do not show them in order to keep the figure uncluttered.)
"Authorized" users connect to the omni AP from within the perimeter.
We assume that internal security procedures handle authentication inside
the perimeter. The problem is that the omni's coverage extends beyond
the physical perimeter.
Figure 1: WIDS access point protecting a perimeter
WIDS addresses this problem because an intruder attempting to break
into the wireless network from the outside will contact the WIDS APs before
coming within range of the omni AP. The WIDS APs will detect intrusions
based on both known attack signatures (such as network probes from NetStumbler
or other non-passive "war-driving" programs) and behavior based
signatures (such as an internal MAC address suddenly appearing in an external
location on a machine with different OS characteristics than it had previously).
WIDS will detect anomalous behavior by tying the signature data into a
behavior-based intrusion detection system [Hofmeyr et al
1998] and [Marceau 2000].
WIDS is based on open-source access point code, HostAP [Malinen
2003], using Prism II wireless card drivers. The capabilities of WIDS
include user-specifiable, trigger-based intrusion detection, allowing the
user to tailor the criteria that trigger alarms. Triggers are based on
both packet/frame data and historical/behavioral data-including typical
signal strength levels, locations, time-to-live (TTL) values, IPIDs, etc.
for particular MAC addresses. For example, we could specify that packets
from MAC 00:20:E0:8C:92:88 must have a TTL of 30 and a signal strength
greater than 10; any packets from this MAC address not meeting those criteria
will raise an alarm. This is similar to a honeypot or honeynet (see http://project.honeynet.org),
but allows more detailed control.
We used the publicly available software HostAP [Malinen
2003], a set of loadable kernel modules and a user-space daemon, under
Linux, for interacting with and configuring the module that make an 802.11b
wireless Ethernet card become an access point (instead of a remote station).
The Zoom Air 4100 series cards feature both the Prism II chipset and an
external antenna plug (a reverse-polarity SMA plug), two requirements for
3.2 Directional Antenna
An intruder's location can be determined by rotating a directional antenna
360 degrees while monitoring the signal strength. Ideally, the signal would
have a single, global maximum representing the direction of the intruder.
Unfortunately, antennas are not ideal due to multipath reflection and other
environmental scattering, resulting in a more complex signal. However,
we can determine the intruder's bearing by measuring an antenna's "signature"
ahead of time, and then comparing the intruder's data to the signature
data. Gathering a full set of data points would take too much time and
delay the response. Therefore, we may collect fewer data points for the
intruder as compared to the signature. We conducted experiments to verify
the accuracy of correlating these two data sets.
We used three different types of directional antennas for the project:
grid array, parabolic dish, and Vagi, described below. Different antenna
types have different signatures but the signature of each individual antenna
type should be independent of the distance to the target (i.e., the distance
will only attenuate the signal and affect its amplitude).
The grid array, a parabolic grid array antenna, is the largest
of the antennas and has the greatest gain. Its specifications are: 8 lbs,
24 dBi gain, 10° beam angle, >28 dB (see http://www.ydi.com/products/pt2421-pt2424.php).
The Vagi, a V-shaped Yagi-style antenna, is the lightest of the
group, with decent gain. Its specifications are: 1.5 lbs, 16 dBi gain,
25° beam angle and 19 dB F/B ratio (see http://www.pacwireless.com/html/vagi_series.html).
The echo, a parabolic dish antenna, is a nice compromise and
has the best F/B (front-to-back) ratio. Its specifications are: 4.4 lbs,
14 dBi gain, 26° beam angle, >30 dB F/B (see http://www.pacwireless.com/html/echo_series.html.).
3.3 IDS Architecture
WIDS Access Points (WIDS AP) are fully functional access points. Security
mechanisms can examine each incoming packet by using the logging facility
The system is configured as follows. The controller knows all the legitimate
MAC addresses and their system profiles. Each antenna knows its scanning
range, its neighbors, and their scanning ranges. Each WIDS AP has a set
of triggers (security alerts) and response mechanisms for the triggers
defined. [Figure 2] shows the components of the IDS.
There are two general states to the IDS operation: startup and steady state.
We describe each below.
Figure 2: Intrusion Detection System Overview
On startup, the controller distributes the list of legitimate nodes,
profiles, and their MACs to the WIDS Access Points (WIDS APs). Then each
WIDS AP initializes its security mechanisms with the coordinates of the
During the steady state, a WIDS AP receives an Association Request (AR)
from an intruder node (outside the perimeter). It then captures the MAC
address. If the MAC address is not in the access control list distributed
by the controller, the information is passed on to the controller to take
appropriate action. Otherwise the controller may request a history (activity
log), if any, of that MAC from the WIDS AP or its neighbors. This includes
the omnidirectional AP as well. The requested information includes last
seen sequence numbers, system profile, and observed activity.
The controller then contacts the neighbors of the reporting WIDS AP
and requests the neighbors to scan for the intruder to accurately determine
its location. Based on the location and signal strength of the intruder,
the controller determines who should monitor the intruder.
The controller then analyzes the frames against the history of that
MAC. This can include information gathering from that node, such as an
OS fingerprint. After the controller has completed processing, it may send
an alert to an operator.. The WIDS AP takes an appropriate response, which
can include redirecting the intruder to a fake machine (a "fishbowl").
3.4 Response Modes
We have defined several response modes: none, locate, connect, and phalanx,
each of which is described below. The configure and command
messages can be used to set the response, with the former setting the default
response and the latter specifying a response for a particular MAC.
None. This is the most basic response mode, in which the WIDS
AP does nothing. It will refuse to accept association messages from the
intruder. Because the WIDS AP refuses associations, the internal omni-directional
AP may accept association requests if the remote station ("intruder")
is within the field of the omni.
Locate. In this response mode, after one WIDS AP detects the
intruder and reports its angle via an alert message, the controller
issues a command message instructing the AP to send a disassociate
message to the intruder and ignore further associate requests, and sends
a command to its neighbor APs instructing them to track the intruder's
MAC address and report on its location. When the WIDS AP sends an alert
message, the controller can determine the location of the intruder using
Connect. The connect response mode instructs the WIDS AP to allow
the intruder to associate with it and will allow it to "connect"
to a network. The WIDS AP will route traffic to a fake network (a "fishbowl,"
sometimes referred to as a "honeypot") where further analyses
on the intruder can be performed. The fishbowl and analyses are performed
by a separate component; the WIDS AP merely routes traffic to that network.
Phalanx. The final response mode defined in the initial prototype
version is the "phalanx" mode. This uses the "fakeAP"
software which generates thousands of virtual, fake access points. By changing
the MAC address, ESSID, and channel every time a beacon is broadcast, fakeAP
can make it appear that there are thousands of other access points, which
makes it difficult for the intruder to detect where the real access point
is. In addition, the intruder must send a lot of packets to find the real
access point, which makes its detection and location easier. Of course,
this mode is the opposite of "run silent, run deep" and would
only be appropriate to use in certain situations.
Track. This response mode is used to track the location of an
intruder and report if the location changes. This mode is similar to the
locate mode, but can have extra triggers. The Track mode can be configured
so that an alert message is sent only if the intruder moves more
than a specified distance or into or out of a specified location.
In the experiments, we computed the remote station and compared it to
the actual location. We performed some initial tests to characterize each
antenna's beam pattern. This data was then used as training information
that would allow us to estimate the incident angle of an intruder by correlation.
For each experiment, we set up a portable WIDS AP, using a telescope tripod
mount and one of the directional antennas described above. A Linux laptop
running HostAP was connected to the antenna under test. An "intruder"
machine was placed a few hundred feet away and allowed to associate with
the WIDS AP. The intruder was also set to ping the AP once per second,
to ensure that some traffic was flowing through the wireless link.
We used the iwspy Linux command to measure the intruder's signal
strength at the WIDS AP. iwspy reports both noise and signal level
in dB, along with a "signal quality" function of unspecified
units. As we wish to find the location of the transmitter (intruder), we
only care about the signal strength: noise level and overall quality are
secondary, and likely to be affected by external factors. The values reported
by iwspy are updated only when traffic from the target is seen,
thus the need for the continuous pinging. We wrote a short Perl script
to collect and timestamp values from iwspy, sorting them by MAC
With the setup complete, we then noted the true angle of the intruder
machine as indicated by the tripod. Working slowly and in fixed increments,
we rotated the directional antenna and noted the signal strength value
reported after a few seconds. Readings were taken over the entire 360°
range to help in characterizing side- and back-lobes.
The tests were performed in a realistic environment in which WIDS might
be deployed, specifically, an office building and adjacent parking lot
with cars present. In the interest of space, we will present only the final
results of our experiments.
One of the primary goals of the WIDS effort is to locate intruders using
the beam patterns of the directional antennas. For each individual antenna,
we first obtain (experimentally) a training beam pattern using the methods
described above. The training data set consists of signal strength measurements
at 1° increments over the entire 360° of antenna rotation. A reference
point for 0° is chosen to be north, though any other convenient 0°-point
can be used. Given a set of signal strength measurements of the intruder's
transmissions, at different angles of the WIDS AP's antenna, we correlate
the training data with these new measurements to determine the intruder's
location (angle). The computed angle is that with the best fit to the known
antenna beam pattern from the training set.
Signal strength measurements from the intruder will not, in general,
occur at 1° increments, nor will there be measurements for even a large
subset of the 360° range, due to physical constraints limiting the
antenna's rotation. In such cases, we interpolate among these non-uniform
measurements to correlate among uniformly-spaced samples. For our initial
work, we use simple linear interpolation. Future work can include more
sophisticated techniques, as well as estimates of the uncertainty introduced
by such interpolation.
After interpolation of test samples, we then compute the correlation
for each possible angular offset:
where strain(n) is the nth training
sample of signal strength versus angle and stest(n) is
the nth test sample of signal strength for this potential
intruder (likely interpolated from measured data); N = 360. Both strain
and stest are normalized by removing their mean and dividing
by their magnitude before performing this calculation; the raw signal power
is not as important in this application as the relative measured powers
at different angles.
As training and test data samples are spaced in 1° increments,
is the correlation at an offset of k degrees.
The offset with the largest correlation is that at which the training
and test samples best match, thus it is our estimated bearing of the target
(assuming the training data's 0° point corresponds to north). The entire
set of correlation values ,
a vector of 360 samples, can be used to estimate uncertainty in the correlation
calculation; larger values indicate more probable intruder bearings, smaller
ones, less probable bearings. Antennas with narrow beam patterns will yield
narrower spikes in the correlation vector, due to their increased resolving
power, while broader antenna beam patterns give broader correlation vectors-i.e.,
less certain angle estimates.
The direct correlation technique described above requires O(N3)
operations to compute the entire
vector. The O(N3) technique executes in approximately
0.3 seconds on a 2.5GHz P4 CPU, more than fast enough for our purposes,
though faster FFT-based techniques could be used [Oppenheim
and Schafer 1989].
5 Experimental Results
[Figure 3] shows Vagi antenna signal strength measurements
in 5° increments, where the target was located about 150 feet from
the directional antenna. The angles have been adjusted so that the target
corresponds to a 0° bearing (making it easier to use the data in later
correlations). The graph shows the effects of real-world obstacles, reflections,
and distortions on what should be an ideal radiation pattern. These effects
prevent pinpointing the exact location of the target using a naïve
approach, such as maximum value.
The target was then positioned at a different angle, about 75 feet from
the WIDS AP on a small hill. Only a few measurements were taken at this
location, shown in [Figure 4]. These measurements simulate
the availability of only a few signal strength samples in deployed versions
Figure 3: Vagi signal strength versus angle, June 16 2003.
Target is at 0 degrees bearing (up), approximately 150 feet from the antenna.
Figure 4: Vagi signal strength versus angle, June 16 2003.
Target is at 330 degrees bearing (upper-left), approximately 75 feet from
5.1 Correlation Results
Despite the temporal variance, physical obstructions and reflections,
and weaker than expected signal strength, the correlation algorithm described
in Section 4.1 worked quite well in pinpointing the target machine's bearing.
For the grid antenna, the 3°-sampled measurements experiment were used
as training data. (In deployed systems, the training data would be an amalgam
of measurements taken under a number of conditions.)
Each of the remaining figures shows the correlation value at each candidate
angle, with 1° increments; in the above equation. As described in Section
4.1, the angle corresponding to the highest correlation value is declared
to be the target angle estimate.
[Fig. 5] shows the correlation versus angle for
the grid antenna measurements when compared with earlier training data.
The antenna was located at a bearing of 25°; our algorithm estimated
the angle to be 13°. The figure shows that 13° corresponds to the
peak in signal response.
Figure 5: Correlation values of the May 9 grid antenna trial
with the April 11 training data; the maximum correlation occurs at 13°.
[Fig. 6] shows the correlation with a later grid
antenna trial, again with the earlier training data. The target was estimated
to be at 23°, whereas it was actually at 25° (two degrees is within
the likely error range of our visual angle measurements).
[Fig. 7] shows a grid antenna trial in which the
target was estimated to be 37°, while the target was sighted at 40°.
Figure 6: Correlation values of the first June 10 grid antenna
trial with the April 11 training data; the maximum correlation occurs at
Figure 7: Correlation values of the second June 10 grid antenna
trial with the April 11 training data; the maximum correlation occurs at
The Vagi antenna trials are correlated in [Fig. 8]
with the first (more detailed) trial as training data, and the second trial's
sporadic measurements as test data. The target was sighted at a bearing
of 330° and the angle estimate was 326°.
The results of [Fig. 8] show a definitive correlation.
It should be noted that even when presented with less than ideal correlation
results, as in [Fig. 7], we can locate the target with
a high degree of accuracy. WIDS was designed to function in a realistic,
noisy environment. These results support that claim.
Figure 8: Correlation values of the June 16 Vagi antenna
trials, with the first trial as training data and the second as testing;
the maximum correlation occurs at 326°.
5.2 Comparison of Antennas
All three antennas, Vagi, grid array, and echo, performed reasonably
close to their marketing specifications. In order to best determine the
accuracy of the correlation calculations, most of our experiments used
the grid array antenna (the most selective). This antenna was physically
the largest and most unwieldy; in practical applications, antenna size
could be an important factor in terms of robustness, visibility to potential
intruders, and cost for accurate positioning.
Cable and connector losses may be problematic as well. The coax cable
between the WIDS AP's wireless card and the grid antenna, while having
a 10 AWG core, is nearly 15 feet in length and contains two intermediate
connectors. The received signal strength measured by the intruder is significantly
(~20 dBm) higher than the received signal strength measured by iwspy
on the WIDS AP (the former being a direct connection, the latter going
through 15 feet of cable).
Other variables, however, prevent us from drawing a direct conclusion:
different software is used on the intruder to measure signal strength,
and the two measurements are of symmetric-but not necessarily identical-signal
strengths. In future work we will quantify the effects of cable attenuation.
An additional correlation experiment uses the Vagi antenna. While not
as sharply directional, the Vagi is significantly lighter and smaller.
Our correlation code does not need a sharp peak in the reception pattern
in order to determine the incident angle; what matter are the fluctuations
in response through the entire 360° sweep.
The three antennas were roughly equal in cost, approximately $80 each.
Performance characteristics and allowable size will be the main factors
that drive the decision of what antenna is most appropriate for an installation.
Most likely, no one antenna will be suitable for all installations.
Overall, the Vagi antenna offers an attractive tradeoff between size/weight
and accuracy, with angle estimates nearly as precise as the grid array.
The needs of a particular installation, however, will dictate which antenna
type to use.
Overall, the antennas' beam patterns match what was expected, with the
grid array antenna being more directional than the other two. The patterns
were not as ideal as those in the antennas' marketing literature, possibly
due to interference and reflections from surrounding objects (particularly
cars). To construct final training sets for each antenna in an installation,
more detailed measurements under varied conditions would be required. Specific
WIDS installations would benefit from training data measured on-site, where
many potential obstacles and reflectors are in place.
We were surprised by the fluctuation in signal strength measurements
reported by iwspy. These fluctuations varied from 2 to up to 8-10
dBm over a few seconds; it seems unlikely that reception conditions change
that rapidly in our environment. iwspy simply uses values reported
by the wireless card driver (Prism, in this case). Our suspicions were
confirmed by measurements taken by using the target machine's "Toshiba
Client Manager Link Test" software under Windows; the target reported
signal strength variance of less than 5 dBm, often 0 (a constant value).
6 Conclusions and Future Work
WIDS provides intrusion detection and intruder location for wireless
access points. This paper focused on the ability to determine the angle
representing the bearing of the remote station to the directional antenna.
We were able to get an accurate angle using inexpensive, readily available
hardware. By using triangulation on the bearings obtained from two directional
antennas at different locations, we accurately determined the location
of the remote station.
WIDS includes intrusion detection capabilities in addition to intruder
location. Users of WIDS will be able to detect network intruders before
they connect to the "real" (non-WIDS) access points. In some
cases, intruders will be detected when they are attempting to scan for
networks (e.g., by using NetStumbler), long before they associate with
the omni access point. By using a combination of signature and behavior
based techniques, WIDS can detect a "spoofed" MAC address (i.e.,
an intruder masquerading as a legitimate user).
Since the signals in wireless networks radiate beyond the intended coverage
area, intruders beyond the physical perimeter can attack the network. WIDS
protects against these attacks.
Currently, WIDS comprises the directional capabilities described in
this paper, as well as triangulation location, an IDS component, a protocol
for WIDS AP communication, and an interface to control the system. Future
work will include taking the proof-of-concept components developed and
integrating them into one prototype. Additional features include adding
motor controls on the directional antennas and increasing the sophistication
of the IDS pattern matching capabilities.
Antenna arrays, an alternative to physically rotating directional antennas,
measure the phase difference of signals incident on each element of the
array. Due to the temporal resolution required to measure phase differences
of 2.4 GHz signals, this is generally done with beamforming techniques.
Current work in this field has focused on cellular telephony applications,
and is often based on algorithms such as MUSIC [Swindlehurst
and Kailath 1992]. Such measurements are impossible within the framework
of HostAP and likely require radio-level access to the 802.11 hardware
or custom hardware of our own.
This material is based upon work supported by the Naval Surface Warfare
Center, Dahlgren Division, under Contract Number N00178-03-C-2010.
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