Network Working Group J. Rosenberg

Network Working Group J. Rosenberg
Request for Comments: 5039 C. Jennings
Category: Informational Cisco
January 2008

The Session Initiation Protocol (SIP) and Spam

Status of This Memo

This memo provides information for the Internet community. It does
not specify an Internet standard of any kind. Distribution of this
memo is unlimited.

Abstract

Spam, defined as the transmission of bulk unsolicited messages, has
plagued Internet email. Unfortunately, spam is not limited to email.
It can affect any system that enables user-to-user communications.
The Session Initiation Protocol (SIP) defines a system for user-to-
user multimedia communications. Therefore, it is susceptible to
spam, just as email is. In this document, we analyze the problem of
spam in SIP. We first identify the ways in which the problem is the
same and the ways in which it is different from email. We then
examine the various possible solutions that have been discussed for
email and consider their applicability to SIP.

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Table of Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Problem Definition . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Call Spam . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2. IM Spam . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3. Presence Spam . . . . . . . . . . . . . . . . . . . . . . 7
3. Solution Space . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1. Content Filtering . . . . . . . . . . . . . . . . . . . . 8
3.2. Black Lists . . . . . . . . . . . . . . . . . . . . . . . 9
3.3. White Lists . . . . . . . . . . . . . . . . . . . . . . . 9
3.4. Consent-Based Communications . . . . . . . . . . . . . . . 10
3.5. Reputation Systems . . . . . . . . . . . . . . . . . . . . 12
3.6. Address Obfuscation . . . . . . . . . . . . . . . . . . . 14
3.7. Limited-Use Addresses . . . . . . . . . . . . . . . . . . 14
3.8. Turing Tests . . . . . . . . . . . . . . . . . . . . . . . 15
3.9. Computational Puzzles . . . . . . . . . . . . . . . . . . 17
3.10. Payments at Risk . . . . . . . . . . . . . . . . . . . . . 17
3.11. Legal Action . . . . . . . . . . . . . . . . . . . . . . . 18
3.12. Circles of Trust . . . . . . . . . . . . . . . . . . . . . 19
3.13. Centralized SIP Providers . . . . . . . . . . . . . . . . 19
4. Authenticated Identity in Email . . . . . . . . . . . . . . . 20
4.1. Sender Checks . . . . . . . . . . . . . . . . . . . . . . 21
4.2. Signature-Based Techniques . . . . . . . . . . . . . . . . 21
5. Authenticated Identity in SIP . . . . . . . . . . . . . . . . 22
6. Framework for Anti-Spam in SIP . . . . . . . . . . . . . . . . 23
7. Additional Work . . . . . . . . . . . . . . . . . . . . . . . 24
8. Security Considerations . . . . . . . . . . . . . . . . . . . 24
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 24
10. Informative References . . . . . . . . . . . . . . . . . . . . 25

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1. Introduction

Spam, defined as the transmission of bulk unsolicited email, has been
a plague on the Internet email system. Many solutions have been
documented and deployed to counter the problem. None of these
solutions is ideal. However, one thing is clear: the spam problem
would be much less significant had solutions been deployed
ubiquitously before the problem became widespread.

The Session Initiation Protocol (SIP) [2] is used for multimedia
communications between users, including voice, video, instant
messaging, and presence. Consequently, it can be just as much of a
target for spam as email. To deal with this, solutions need to be
defined and recommendations put into place for dealing with spam as
soon as possible.

This document serves to meet those goals by defining the problem
space more concretely, analyzing the applicability of solutions used
in the email space, identifying protocol mechanisms that have been
defined for SIP that can help the problem, and making recommendations
for implementors.

2. Problem Definition

The spam problem in email is well understood, and we make no attempt
to further elaborate on it here. The question, however, is what is
the meaning of spam when applied to SIP? Since SIP covers a broad
range of functionality, there appear to be three related but
different manifestations:

Call Spam: This type of spam is defined as a bulk unsolicited set of
session initiation attempts (i.e., INVITE requests), attempting to
establish a voice, video, instant messaging [1], or other type of
communications session. If the user should answer, the spammer
proceeds to relay their message over the real-time media. This is
the classic telemarketer spam, applied to SIP. This is often
called SPam over Ip Telephony, or SPIT.

IM Spam: This type of spam is similar to email. It is defined as a
bulk unsolicited set of instant messages, whose content contains
the message that the spammer is seeking to convey. IM spam is
most naturally sent using the SIP MESSAGE [3] request. However,
any other request that causes content to automatically appear on
the user's display will also suffice. That might include INVITE
requests with large Subject headers (since the Subject is
sometimes rendered to the user), or INVITE requests with text or
HTML bodies. This is often called SPam over Instant Messaging, or
SPIM.

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Presence Spam: This type of spam is similar to IM spam. It is
defined as a bulk unsolicited set of presence requests (i.e.,
SUBSCRIBE requests [4] for the presence event package [6]), in an
attempt to get on the "buddy list" or "white list" of a user in
order to send them IM or initiate other forms of communications.
This is occasionally called SPam over Presence Protocol, or SPPP.

There are many other SIP messages that a spammer might send.
However, most of the other ones do not result in content being
delivered to a user, nor do they seek input from a user. Rather,
they are answered by automata. OPTIONS is a good example of this.
There is little value for a spammer in sending an OPTIONS request,
since it is answered automatically by the User Agent Server (UAS).
No content is delivered to the user, and they are not consulted.

In the sections below, we consider the likelihood of these various
forms of SIP spam. This is done in some cases by a rough cost
analysis. It should be noted that all of these analyses are
approximate, and serve only to give a rough sense of the order of
magnitude of the problem.

2.1. Call Spam

Will call spam occur? That is an important question to answer.
Clearly, it does occur in the existing telephone network, in the form
of telemarketer calls. Although these calls are annoying, they do
not arrive in the same kind of volume as email spam. The difference
is cost; it costs more for the spammer to make a phone call than it
does to send email. This cost manifests itself in terms of the cost
for systems that can perform telemarketer call, and in cost per call.

Both of these costs are substantially reduced by SIP. A SIP call
spam application is easy to write. It is just a SIP User Agent that
initiates, in parallel, a large number of calls. If a call connects,
the spam application generates an ACK and proceeds to play out a
recorded announcement, and then it terminates the call. This kind of
application can be built entirely in software, using readily
available (and indeed, free) off-the-shelf software components. It
can run on a low-end PC and requires no special expertise to execute.

The cost per call is also substantially reduced. A normal
residential phone line allows only one call to be placed at a time.
If additional lines are required, a user must purchase more expensive
connectivity. Typically, a T1 or T3 would be required for a large-
volume telemarketing service. That kind of access is very expensive
and well beyond the reach of an average user. A T1 line is
approximately US $250 per month, and about 1.5 cents per minute for
calls. T1 lines used only for outbound calls (such as in this case)

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are even more expensive than inbound trunks due to the reciprocal
termination charges that a provider pays and receives.

There are two aspects to the capacity: the call attempt rate, and the
number of simultaneous successful calls that can be in progress. A
T1 would allow a spammer, at most, 24 simultaneous calls, and
assuming about 10 seconds for each call attempt, about 2.4 call
attempts per second. At high-volume calling, the per-minute rates
far exceed the flat monthly fee for the T1. The result is a cost of
250,000 microcents for each successful spam delivery, assuming 10
seconds of content.

With SIP, this cost is much reduced. Consider a spammer using a
typical broadband Internet connection that provides 500 Kbps of
upstream bandwidth. Initiating a call requires just a single INVITE
message. Assuming, for simplicity's sake, that this is 1 KB, a 500
Kbps upstream DSL or cable modem connection will allow about 62 call
attempts per second. A successful call requires enough bandwidth to
transmit a message to the receiver. Assuming a low compression codec
(say, G.723.1 at 5.3 Kbps), this requires approximately 16 Kbps after
RTP, UDP, and IP overheads. With 500 Kbps upstream bandwidth, this
means as many as 31 simultaneous calls can be in progress. With 10
seconds of content per call, that allows for 3.1 successful call
attempts per second. If broadband access is around $50/month, the
cost per successful voice spam is about 6.22 microcents each. This
assumes that calls can be made 24 hours a day, 30 days a month, which
may or may not be the case.

These figures indicate that SIP call spam is roughly four orders of
magnitude cheaper to send than traditional circuit-based telemarketer
calls. This low cost is certainly going to be very attractive to
spammers. Indeed, many spammers utilize computational and bandwidth
resources provided by others, by infecting their machines with
viruses that turn them into "zombies" that can be used to generate
spam. This can reduce the cost of call spam to nearly zero.

Even ignoring the zombie issue, this reduction in cost is even more
amplified for international calls. Currently, there are few
telemarketing calls across international borders, largely due to the
large cost of making international calls. This is one of the reasons
why the "do not call list", a United States national list of numbers
that telemarketers cannot call -- has been effective. The law only
affects U.S. companies, but since most telemarketing calls are
domestic, it has been effective. Unfortunately (and fortunately),
the IP network provides no boundaries of these sorts, and calls to
any SIP URI are possible from anywhere in the world. This will allow
for international spam at a significantly reduced cost.

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International spam is likely to be even more annoying than national
spam, since it may arrive in languages that the recipient doesn't
even speak.

These figures assume that the primary limitation is the access
bandwidth and not CPU, disk, or termination costs. Termination costs
merit further discussion. Currently, most Voice over IP (VoIP) calls
terminate on the Public Switched Telephone Network (PSTN), and this
termination costs the originator of the call money. These costs are
similar to the per-minute rates of a T1. It ranges anywhere from
half a cent to three cents per minute, depending on volume and other
factors. However, equipment costs, training, and other factors are
much lower for SIP-based termination than a T1, making the cost still
lower than circuit connectivity. Furthermore, the current trend in
VoIP systems is to make termination free for calls that never touch
the PSTN, that is, calls to actual SIP endpoints. Thus, as more and
more SIP endpoints come online, termination costs will probably drop.
Until then, SIP spam can be used in concert with termination services
for a lower-cost form of traditional telemarketer calls, made to
normal PSTN endpoints.

It is useful to compare these figures with email. VoIP can deliver
approximately 3.1 successful call attempts per second. Email spam
can, of course, deliver more. Assuming 1 KB per email, and an
upstream link of 500 Kbps, a spammer can generate 62.5 messages per
second. This number goes down with larger messages of course.
Interestingly, spam filters delete large numbers of these mails, so
the cost per viewed message is likely to be much higher. In that
sense, call spam is much more attractive, since its content is much
more likely to be examined by a user if a call attempt is successful.

Another part of the cost of spamming is collecting addresses.
Spammers have, over time, built up immense lists of email addresses,
each of the form user@domain, to which spam is directed. SIP uses
the same form of addressing, making it likely that email addresses
can easily be turned into valid SIP addresses. Telephone numbers
also represent valid SIP addresses; in concert with a termination
provider, a spammer can direct SIP calls at traditional PSTN devices.
It is not clear whether email spammers have also been collecting
phone numbers as they perform their Web sweeps, but it is probably
not hard to do so. Furthermore, unlike email addresses, phone
numbers are a finite address space and one that is fairly densely
packed. As a result, going sequentially through phone numbers is
likely to produce a fairly high hit rate. Thus, it seems like the
cost is relatively low for a spammer to obtain large numbers of SIP
addresses to which spam can be directed.

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2.2. IM Spam

IM spam is very much like email, in terms of the costs for deploying
and generating spam. Assuming, for the sake of argument, a 1KB
message to be sent and 500 Kbps of upstream bandwidth, that is 62.5
messages per second. At $50/month, the result is .31 microcents per
message. This is less than voice spam, but not substantially less.
The cost is probably on par with email spam. However, IM is much
more intrusive than email. In today's systems, IMs automatically pop
up and present themselves to the user. Email, of course, must be
deliberately selected and displayed. However, most popular IM
systems employ white lists, which only allow IM to be delivered if
the sender is on the white list. Thus, whether or not IM spam will
be useful seems to depend a lot on the nature of the systems as the
network is opened up. If they are ubiquitously deployed with white-
list access, the value of IM spam is likely to be low.

It is important to point out that there are two different types of IM
systems: page mode and session mode. Page mode IM systems work much
like email, with each IM being sent as a separate message. In
session mode IM, there is signaling in advance of communication to
establish a session, and then IMs are exchanged, perhaps point-to-
point, as part of the session. The modality impacts the types of
spam techniques that can be applied. Techniques for email can be
applied identically to page mode IM, but session mode IM is more like
telephony, and many techniques (such as content filtering) are harder
to apply.

2.3. Presence Spam

As defined above, presence spam is the generation of bulk unsolicited
SUBSCRIBE messages. The cost of this is within a small constant
factor of IM spam so the same cost estimates can be used here. What
would be the effect of such spam? Most presence systems provide some
kind of consent framework. A watcher that has not been granted
permission to see the user's presence will not gain access to their
presence. However, the presence request is usually noted and
conveyed to the user, allowing them to approve or deny the request.
In SIP, this is done using the watcherinfo event package [7]. This
package allows a user to learn the identity of the watcher, in order
to make an authorization decision.

Interestingly, this provides a vehicle for conveying information to a
user. By generating SUBSCRIBE requests from identities such as
sip:[email protected], brief messages can be
conveyed to the user, even though the sender does not have, and never
will receive, permission to access presence. As such, presence spam
can be viewed as a form of IM spam, where the amount of content to be

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conveyed is limited. The limit is equal to the amount of information
generated by the watcher that gets conveyed to the user through the
permission system.

This type of spam also shows up in consent frameworks used to prevent
call spam, as discussed in Section 3.4.

3. Solution Space

In this section, we consider the various solutions that might be
possible to deal with SIP spam. We primarily consider techniques
that have been employed to deal with email spam. It is important to
note that the solutions documented below are not meant to be an
exhaustive study of the spam solutions used for email but rather just
a representative set. We also consider some solutions that appear to
be SIP-specific.

3.1. Content Filtering

The most common form of spam protection used in email is based on
content filtering. Spam filters analyze the content of the email,
and look for clues that the email is spam. Bayesian spam filters are
in this category.

Unfortunately, this type of spam filtering, while successful for
email spam, is completely useless for call spam. There are two
reasons. First, in the case where the user answers the call, the
call is already established and the user is paying attention before
the content is delivered. The spam cannot be analyzed before the
user sees it. Second, if the content is stored before the user
accesses it (e.g., with voicemail), the content will be in the form
of recorded audio or video. Speech and video recognition technology
is not likely to be good enough to analyze the content and determine
whether or not it is spam. Indeed, if a system tried to perform
speech recognition on a recording in order to perform such an
analysis, it would be easy for the spammers to make calls with
background noises, poor grammar, and varied accents, all of which
will throw off recognition systems. Video recognition is even harder
to do and remains primarily an area of research.

IM spam, due to its similarity to email, can be countered with
content analysis tools. Indeed, the same tools and techniques used
for email will directly work for IM spam.

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3.2. Black Lists

Black listing is an approach whereby the spam filter maintains a list
of addresses that identify spammers. These addresses include both
usernames ([email protected]) and entire domains (example.com).
Pure blacklists are not very effective in email for two reasons.
First, email addresses are easy to spoof, making it easy for the
sender to pretend to be someone else. If the sender varies the
addresses they send from, the black list becomes almost completely
useless. The second problem is that, even if the sender doesn't
forge the From address, email addresses are in almost limitless
supply. Each domain contains an infinite supply of email addresses,
and new domains can be obtained for very low cost. Furthermore,
there will always be public providers that will allow users to obtain
identities for almost no cost (for example, Yahoo or AOL mail
accounts). The entire domain cannot be blacklisted because it
contains so many valid users. Blacklisting needs to be for
individual users. Those identities are easily changed.

As a result, as long as identities are easy to manufacture, or
zombies are used, black lists will have limited effectiveness for
email.

Blacklists are also likely to be ineffective for SIP spam.
Mechanisms for inter-domain authenticated identity for email and SIP
are discussed in Section 4 and Section 5. Assuming these mechanisms
are used and enabled in inter-domain communications, it becomes
difficult to forge sender addresses. However, it still remains cheap
to obtain a nearly infinite supply of addresses.

3.3. White Lists

White lists are the opposite of black lists. It is a list of valid
senders that a user is willing to accept email from. Unlike black
lists, a spammer cannot change identities to get around the white
list. White lists are susceptible to address spoofing, but a strong
identity authentication mechanism can prevent that problem. As a
result, the combination of white lists and strong identity, as
described in Section 4.2 and Section 5, are a good form of defense
against spam.

However, they are not a complete solution, since they would prohibit
a user from ever being able to receive email from someone who was not
explicitly put on the white list. As a result, white lists require a
solution to the "introduction problem" - how to meet someone for the
first time, and decide whether they should be placed in the white
list. In addition to the introduction problem, white lists demand
time from the user to manage.

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In IM systems, white lists have proven exceptionally useful at
preventing spam. This is due, in no small part, to the fact that the
white list exists naturally in the form of the buddy list. Users
don't have to manage this list just for the purposes of spam
prevention; it provides general utility, and assists in spam
prevention for free. Many popular IM systems also have strong
identity mechanisms since they do not allow communications with IM
systems in other administrative domains. The introduction problem in
these systems is solved with a consent framework, described below.

The success of white lists in IM systems has applicability to SIP as
well. This is because SIP also provides a buddy list concept and has
an advanced presence system as part of its specifications. The
introduction problem remains. In email, techniques like Turing tests
have been employed to address the introduction problem. Turing tests
are considered further in the sections below. As with email, a
technique for solving the introduction problem would need to be
applied in conjunction with a white list.

If a user's computer is compromised and used a zombie, that computer
can usually be used to send spam to anyone that has put the user on
their white list.

3.4. Consent-Based Communications

A consent-based solution is used in conjunction with white or black
lists. That is, if user A is not on user B's white or black list,
and user A attempts to communicate with user B, user A's attempt is
initially rejected, and they are told that consent is being
requested. Next time user B connects, user B is informed that user A
had attempted communications. User B can then authorize or reject
user A.

These kinds of consent-based systems are used widely in presence and
IM. Since most of today's popular IM systems only allow
communications within a single administrative domain, sender
identities can be authenticated. Email often uses similar consent-
based systems for mailing lists. They use a form of authentication
based on sending cookies to an email address to verify that a user
can receive mail at that address.

This kind of consent-based communications has been standardized in
SIP for presence, using the watcher information event package [7] and
data format [8], which allow a user to find out that someone has
subscribed. Then, the XML Configuration Access Protocol (XCAP) [10]
is used, along with the XML format for presence authorization [11] to
provide permission for the user to communicate.

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A consent framework has also been developed that is applicable to
other forms of SIP communications [12]. However, this framework
focuses on authorizing the addition of users to "mailing lists",
known as exploders in SIP terminology. Though spammers typically use
such exploder functions, presumably one run by a spammer would not
use this technique. Consequently, this consent framework is not
directly applicable to the spam problem. It is, however, useful as a
tool for managing a white list. Through the PUBLISH mechanism, it
allows a user to upload a permission document [13] that indicates
that they will only accept incoming calls from a particular sender.

Can a consent framework, like the ones used for presence, help solve
call spam? At first glance, it would seem to help a lot. However,
it might just change the nature of the spam. Instead of being
bothered with content, in the form of call spam or IM spam, users are
bothered with consent requests. A user's "communications inbox"
might instead be filled with requests for communications from a
multiplicity of users. Those requests for communications don't
convey much useful content to the user, but they can convey some. At
the very least, they will convey the identity of the requester. The
user part of the SIP URI allows for limited free form text, and thus
could be used to convey brief messages. One can imagine receiving
consent requests with identities like
"sip:[email protected]", for
example. Fortunately, it is possible to apply traditional content
filtering systems to the header fields in the SIP messages, thus
reducing these kinds of consent request attacks.

In order for the spammer to convey more extensive content to the
user, the user must explicitly accept the request, and only then can
the spammer convey the full content. This is unlike email spam,
where, even though much spam is automatically deleted, some
percentage of the content does get through, and is seen by users,
without their explicit consent that they want to see it. Thus, if
consent is required first, the value in sending spam is reduced, and
perhaps it will cease for those spam cases where consent is not given
to spammers.

As such, the real question is whether or not the consent system would
make it possible for a user to give consent to non-spammers and
reject spammers. Authenticated identity can help. A user in an
enterprise would know to give consent to senders in other enterprises
in the same industry, for example. However, in the consumer space,
if sip:[email protected] tries to communicate with a user, how does
that user determine whether Bob is a spammer or a long-lost friend
from high school? There is no way based on the identity alone. In
such a case, a useful technique is to grant permission for Bob to
communicate but to ensure that the permission is extremely limited.

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In particular, Bob may be granted permission to send no more than 200
words of text in a single IM, which he can use to identify himself,
so that the user can determine whether or not more permissions are
appropriate. It may even be possible that an automated system could
do some form of content analysis on this initial short message.
However, this 200 words of text may be enough for a spammer to convey
their message, in much the same way they might convey it in the user
part of the SIP URI.

Thus, it seems that a consent-based framework, along with white lists
and black lists, cannot fully solve the problem for SIP, although it
does appear to help.

3.5. Reputation Systems

A reputation system is also used in conjunction with white or black
lists. Assume that user A is not on user B's white list, and A
attempts to contact user B. If a consent-based system is used, B is
prompted to consent to communications from A, and along with the
consent, a reputation score might be displayed in order to help B
decide whether or not they should accept communications from A.

Traditionally, reputation systems are implemented in highly
centralized messaging architectures; the most widespread reputation
systems in messaging today have been deployed by monolithic instant
messaging providers (though many Web sites with a high degree of
interactivity employ very similar concepts of reputation).
Reputation is calculated based on user feedback. For example, a
button on the user interface of the messaging client might empower
users to inform the system that a particular user is abusive. Of
course, the input of any single user has to be insufficient to ruin
one's reputation, but consistent negative feedback would give the
abusive user a negative reputation score.

Reputation systems have been successful in systems where
centralization of resources (user identities, authentication, etc.)
and monolithic control dominate. Examples of these include the large
instant messaging providers that run IM systems that do not exchange
messages with other administrative domains. That control, first of
all, provides a relatively strong identity assertion for users (since
all users trust a common provider, and the common provider is the
arbiter of authentication and identity). Secondly, it provides a
single place where reputation can be managed.

Reputation systems based on negative reputation scores suffer from
many of the same problems as black lists, since effectively the
consequence of having a negative reputation is that you are
blacklisted. If identities are very easy to acquire, a user with a

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negative reputation will simply acquire a new identity. Moreover,
negative reputation is generated by tattling, which requires users to
be annoyed enough to click the warning button -- a process that can
be abused. In some reputation systems, "reputation mafias"
consisting of large numbers of users routinely bully or extort
victims by threatening collectively to give victims a negative
reputation.

Reputation systems based on positive reputation, where users praise
each other for being good, rather than tattling on each other for
being bad, have some similar drawbacks. Collectives of spammers, or
just one spammer who acquires a large number identities, could praise
one another in order to create an artificial positive reputation.
Users similarly have to overcome the inertia required to press the
"praise" button. Unlike negative reputation systems, however,
positive reputation is not circumvented when users acquire a new
identity, since basing authorization decisions on positive reputation
is essentially a form of white listing.

So, while positive reputation systems are superior to negative
reputation systems, they are far from perfect. Intriguingly, though,
combining presence-based systems with reputation systems leads to an
interesting fusion. The "buddy-list" concept of presence is, in
effect, a white list - and one can infer that the users on one's
buddy list are people whom you are "praising". This eliminates the
problem of user inertia in the use of the "praise" button, and
automates the initial establishment of reputation.

And of course, your buddies in turn have buddies. Collectively, you
and your buddies (and their buddies, and so on) constitute a social
network of reputation. If there were a way to leverage this social
network, it would eliminate the need for centralization of the
reputation system. Your perception of a particular user's reputation
might be dependent on your relationship to them in the social
network: are they one buddy removed (strong reputation), four buddies
removed (weaker reputation), three buddies removed but connected to
you through several of your buddies, etc. This web of trust
furthermore would have the very desirable property that circles of
spammers adding one another to their own buddy lists would not affect
your perception of their reputation unless their circle linked to
your own social network.

If a users machine is compromised and turned into a zombie, this
allows SPAM to be sent and may impact their reputation in a negative
way. Once their reputation decreases, it becomes extremely difficult
to reestablish a positive reputation.

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3.6. Address Obfuscation

Spammers build up their spam lists by gathering email addresses from
Web sites and other public sources of information. One way to
minimize spam is to make your address difficult or impossible to
gather. Spam bots typically look for text in pages of the form
"user@domain", and assume that anything of that form is an email
address. To hide from such spam bots, many Web sites have recently
begun placing email addresses in an obfuscated form, usable to humans
but difficult for an automata to read as an email address. Examples
include forms such as, "user at example dot com" or "j d r o s e n a
t e x a m p l e d o t c o m".

These techniques are equally applicable to prevention of SIP spam,
and are likely to be as equally effective or ineffective in its
prevention.

It is worth mentioning that the source of addresses need not be a Web
site - any publicly accessible service containing addresses will
suffice. As a result, ENUM [9] has been cited as a potential gold
mine for spammers. It would allow a spammer to collect SIP and other
URIs by traversing the tree in e164.arpa and mining it for data.
This problem is mitigated in part if only number prefixes, as opposed
to actual numbers, appear in the DNS. Even in that case, however, it
provides a technique for a spammer to learn which phone numbers are
reachable through cheaper direct SIP connectivity.

3.7. Limited-Use Addresses

A related technique to address obfuscation is limited-use addresses.
In this technique, a user has a large number of email addresses at
their disposal, each of which has constraints on its applicability.
A limited-use address can be time-bound, so that it expires after a
fixed period. Or, a different email address can be given to each
correspondent. When spam arrives from that correspondent, the
limited-use address they were given is terminated. In another
variation, the same limited-use address is given to multiple users
that share some property; for example, all work colleagues, all
coworkers from different companies, all retailers, and so on. Should
spam begin arriving on one of the addresses, it is invalidated,
preventing communications from anyone else that received the limited
use address.

This technique is equally applicable to SIP. One of the drawbacks of
the approach is that it can make it hard for people to reach you; if
an email address you hand out to a friend becomes spammed, changing
it requires you to inform your friend of the new address. SIP can
help solve this problem in part, by making use of presence [6].

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Instead of handing out your email address to your friends, you would
hand out your presence URI. When a friend wants to send you an
email, they subscribe to your presence (indeed, they are likely to be
continuously subscribed from a buddy list application). The presence
data can include an email address where you can be reached. This
email address can be obfuscated and be of single use, different for
each buddy who requests your presence. They can also be constantly
changed, as these changes are pushed directly to your buddies. In a
sense, the buddy list represents an automatically updated address
book, and would therefore eliminate the problem.

Another approach is to give a different address to each and every
correspondent, so that it is never necessary to tell a "good" user
that an address needs to be changed. This is an extreme form of
limited-use addresses, which can be called a single-use address.
Mechanisms are available in SIP for the generation of [16] an
infinite supply of single use addresses. However, the hard part
remains a useful mechanism for distribution and management of those
addresses.

3.8. Turing Tests

In email, Turing tests are mechanisms whereby the sender of the
message is given some kind of puzzle or challenge, which only a human
can answer (since Turing tests rely on video or audio puzzles, they
sometimes cannot be solved by individuals with handicaps). These
tests are also known as captchas (Completely Automated Public Turing
test to tell Computers and Humans Apart). If the puzzle is answered
correctly, the sender is placed on the user's white list. These
puzzles frequently take the form of recognizing a word or sequence of
numbers in an image with a lot of background noise. The tests need
to be designed such that automata cannot easily perform the image
recognition needed to extract the word or number sequence, but a
human user usually can. Designing such tests is not easy, since
ongoing advances in image processing and artificial intelligence
continually raise the bar. Consequently, the effectiveness of
captchas are tied to whether spammers can come up with or obtain
algorithms for automatically solving them.

Like many of the other email techniques, Turing tests are dependent
on sender identity, which cannot easily be authenticated in email.

Turing tests can be used to prevent IM spam in much the same way they
can be used to prevent email spam.

Turing tests can be applied to call spam as well, although not
directly, because call spam does not usually involve the transfer of
images and other content that can be used to verify that a human is

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on the other end. If most of the calls are voice, the technique
needs to be adapted to voice. This is not that difficult to do.
Here is how it could be done. User A calls user B and is not on user
B's white or black list. User A is transferred to an Interactive
Voice Response (IVR) system. The IVR system tells the user that they
are going to hear a series of numbers (say 5 of them), and that they
have to enter those numbers on the keypad. The IVR system reads out
the numbers while background music is playing, making it difficult
for an automated speech recognition system to be applied to the
media. The user then enters the numbers on their keypad. If they
are entered correctly, the user is added to the white list.

This kind of voice-based Turing test is easily extended to a variety
of media, such as video and text, and user interfaces by making use
of the SIP application interaction framework [14]. This framework
allows client devices to interact with applications in the network,
where such interaction is done with stimulus signaling, including
keypads (supported with the Keypad Markup Language [15]), but also
including Web browsers, voice recognition, and so on. The framework
allows the application to determine the media capabilities of the
device (or user, in cases where they are handicapped) and interact
with them appropriately.

In the case of voice, the Turing test would need to be made to run in
the language of the caller. This is possible in SIP, using the
Accept-Language header field, though this is not widely used at the
moment, and meant for languages of SIP message components, not the
media streams.

The primary problem with the voice Turing test is the same one that
email tests have: instead of having an automata process the test, a
spammer can pay cheap workers to take the tests. Assuming cheap
labor in a poor country can be obtained for about 60 cents per hour,
and assuming a Turing test of a 30-second duration, this is about
0.50 cents per test and thus 0.50 cents per message to send an IM
spam. Lower labor rates would reduce this further; the number quoted
here is based on real online bids in September of 2006 made for
actual work of this type.

As an alternative to paying cheap workers to take the tests, the
tests can be taken by human users that are tricked into completing
the tests in order to gain access to what they believe is a
legitimate resource. This was done by a spambot that posted the
tests on a pornography site, and required users to complete the tests
in order to gain access to content.

Due to these limitations, Turing tests may never completely solve the
problem.

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3.9. Computational Puzzles

This technique is similar to Turing tests. When user A tries to
communicate with user B, user B asks user A to perform a computation
and pass the result back. This computation has to be something a
human user cannot perform and something expensive enough to increase
user A's cost to communicate. This cost increase has to be high
enough to make it prohibitively expensive for spammers but
inconsequential for legitimate users.

One of the problems with the technique is that there is wide
variation in the computational power of the various clients that
might legitimately communicate. The CPU speed on a low-end cell
phone is around 50 MHz, while a high-end PC approaches 5 GHz. This
represents almost two orders of magnitude difference. Thus, if the
test is designed to be reasonable for a cell phone to perform, it is
two orders of magnitude cheaper to perform for a spammer on a high-
end machine. Recent research has focused on defining computational
puzzles that challenge the CPU/memory bandwidth, as opposed to just
the CPU [26]. It seems that there is less variety in the CPU/memory
bandwidth across devices, roughly a single order of magnitude.

Recent work [28] suggests that, due to the ability of spammers to use
virus-infected machines (also known as zombies) to generate the spam,
the amount of computational power available to the spammers is
substantial, and it may be impossible to have them compute a puzzle
that is sufficiently hard that will not also block normal emails. If
combined with white listing, computational puzzles would only be
utilized for new communications partners. Of course, if the partner
on the white list is a zombie, spam will come from that source. The
frequency of communications with new partners is arguably higher for
email than for multimedia, and thus the computational puzzle
techniques may be more effective for SIP than for email in dealing
with the introduction problem.

These techniques are an active area of research right now, and any
results for email are likely to be usable for SIP.

3.10. Payments at Risk

This approach has been proposed for email [27]. When user A sends
email to user B, user A deposits a small amount of money (say, one
dollar) into user B's account. If user B decides that the message is
not spam, user B refunds this money back to user A. If the message
is spam, user B keeps the money. This technique requires two
transactions to complete: a transfer from A to B, and a transfer from
B back to A. The first transfer has to occur before the message can
be received in order to avoid reuse of "pending payments" across

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several messages, which would eliminate the utility of the solution.
The second one then needs to occur when the message is found not to
be spam.

This technique appears just as applicable to call spam and IM spam as
it is to email spam. Like many of the other techniques, this
exchange would only happen the first time you talk to people. Its
proper operation therefore requires a good authenticated identity
infrastructure.

This technique has the potential to make it arbitrarily expensive to
send spam of any sort. However, it relies on cheap micro-payment
techniques on the Internet. Traditional costs for Internet payments
are around 25 cents per transaction, which would probably be
prohibitive. However, recent providers have been willing to charge
15% of the transaction for small transactions, as small as one cent.
This cost would have to be shouldered by users of the system. The
cost that would need to be shouldered per user is equal to the number
of messages from unknown senders (that is, senders not on the white
list) that are received. For a busy user, assume about 10 new
senders per day. If the deposit is 5 cents, the transaction provider
would take 0.75 cents and deliver 4.25 cents. If the sender is
allowed, the recipient returns 4.25 cents, the provider takes 0.64
cents, and returns 3.6 cents. This costs the sender 0.65 cents on
each transaction, if it was legitimate. If there are ten new
recipients per day, that is US $1.95 per month, which is relatively
inexpensive.

Assuming a micro-payment infrastructure exists, another problem with
payment-at-risk is that it loses effectiveness when there are strong
inequities in the value of currency between sender and recipient.
For example, a poor person in a Third World country might keep the
money in each mail message, regardless of whether it is spam.
Similarly, a poor person might not be willing to include money in an
email, even if legitimate, for fear that the recipient might keep it.
If the amount of money is lowered to help handle these problems, it
might become sufficiently small that spammers can just afford to
spend it.

3.11. Legal Action

In this solution, countries pass laws that prohibit spam. These laws
could apply to IM or call spam just as easily as they could apply to
email spam. There is a lot of debate about whether these laws would
really be effective in preventing spam.

As a recent example in the US, "do not call" lists seem to be
effective. However, due to the current cost of long-distance phone

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calls, the telemarketing is coming from companies within the US. As
such, calls from such telemarketers can be traced. If a telemarketer
violates the "do not call" list, the trace allows legal action to be
taken against them. A similar "do not irritate" list for VoIP or for
email would be less likely to work because the spam is likely to come
from international sources. This problem could be obviated if there
was a strong way to identify the sender's legal entity, and then
determine whether it was in a jurisdiction where it was practical to
take legal action against them. If the spammer is not in such a
jurisdiction, the SIP spam could be rejected.

There are also schemes that cause laws other than anti-spam laws to
be broken if spam is sent. This does not inherently reduce SPAM, but
it allows more legal options to be brought to bear against the
spammer. For example, Habeas <http://www.habeas.com> inserts
material in the header that, if it was inserted by a spammer without
an appropriate license, would allegedly causes the spammer to violate
US copyright and trademark laws, possibly reciprocal laws, and
similar laws in many countries.

3.12. Circles of Trust

In this model, a group of domains (e.g., a set of enterprises) all
get together. They agree to exchange SIP calls amongst each other,
and they also agree to introduce a fine should any one of them be
caught spamming. Each company would then enact measures to terminate
employees who spam from their accounts.

This technique relies on secure inter-domain authentication - that
is, domain B can know that messages are received from domain A. In
SIP, this is readily provided by usage of the mutually authenticated
Transport Level Security (TLS)[22] between providers or SIP Identity
[17].

This kind of technique works well for small domains or small sets of
providers, where these policies can be easily enforced. However, it
is unclear how well it scales up. Could a very large domain truly
prevent its users from spamming? At what point would the network be
large enough that it would be worthwhile to send spam and just pay
the fine? How would the pricing be structured to allow both small
and large domains alike to participate?

3.13. Centralized SIP Providers

This technique is a variation on the circles of trust described in
Section 3.12. A small number of providers get established as "inter-
domain SIP providers". These providers act as a SIP-equivalent to
the interexchange carriers in the PSTN. Every enterprise, consumer

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SIP provider, or other SIP network (call these the local SIP
providers) connects to one of these inter-domain providers. The
local SIP providers only accept SIP messages from their chosen inter-
domain provider. The inter-domain provider charges the local
provider, per SIP message, for the delivery of SIP messages to other
local providers. The local provider can choose to pass on this cost
to its own customers if it so chooses.

The inter-domain SIP providers then form bi-lateral agreements with
each other, exchanging SIP messages according to strict contracts.
These contracts require that each of the inter-domain providers be
responsible for charging a minimum per-message fee to their own
customers. Extensive auditing procedures can be put into place to
verify this. Besides such contracts, there may or may not be a flow
of funds between the inter-domain providers.

The result of such a system is that a fixed cost can be associated
with sending a SIP message, and that this cost does not require
micro-payments to be exchanged between local providers, as it does in
Section 3.10. Since all of the relationships are pre-established and
negotiated, cheaper techniques for monetary transactions (such as
monthly post-paid transactions) can be used.

This technique can be made to work in SIP, whereas it cannot in
email, because inter-domain SIP connectivity has not yet been broadly
established. In email, there already exists a no-cost form of inter-
domain connectivity that cannot be eliminated without destroying the
utility of email. If, however, SIP inter-domain communications get
established from the start using this structure, there is a path to
deployment.

This structure is more or less the same as the one in place for the
PSTN today, and since there is relatively little spam on the PSTN
(compared to email!), there is some proof that this kind of
arrangement can work. However, centralized architectures as these
are deliberately eschewed because they put back into SIP much of the
complexity and monopolistic structures that the protocol aims to
eliminate.

4. Authenticated Identity in Email

Though not a form of anti-spam in and of itself, authenticated or
verifiable identities are a key part of making other anti-spam
mechanisms work. Many of the techniques described above are most
effective when combined with a white or black list, which itself
requires a strong form of identity.

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In email, two types of authenticated identity have been developed -
sender checks and signature-based solutions.

4.1. Sender Checks

In email, DNS resource records have been defined that will allow a
domain that receives a message to verify that the sender is a valid
Message Transfer Agent (MTA) for the sending domain [18] [19] [20]
[21]. They don't prevent spam by themselves, but may help in
preventing spoofed emails. As has been mentioned several times, a
form of strong authenticated identity is key in making many other
anti-spam techniques work.

Are these techniques useful for SIP? They can be used for SIP but
are not necessary. In SIP, TLS with mutual authentication can be
used inter-domain. A provider receiving a message can then reject
any message coming from a domain that does not match the asserted
identity of the sender of the message. Such a policy only works in
the "trapezoid" model of SIP, whereby there are only two domains in
any call - the sending domain, which is where the originator resides,
and the receiving domain. These techniques are discussed in Section
26.3.2.2 of RFC 3261 [2]. In forwarding situations, the assumption
no longer holds and these techniques no longer work. However, the
authenticated identity mechanism for SIP, discussed in Section 5,
does work in more complex network configurations and provides fairly
strong assertion of identity.

4.2. Signature-Based Techniques

Domain Keys Identified Mail (DKIM) Signatures [23] (and several non-
standard techniques that preceded it) provide strong identity
assertions by allowing the sending domain to sign an email, and then
providing mechanisms by which the receiving MTA or Mail User Agent
(MUA) can validate the signature.

Unfortunately, when used with blacklists, this kind of authenticated
identity is only as useful as the fraction of the emails that utilize
it. This is partly true for white lists as well; if any
unauthenticated email is accepted for an address on a white list, a
spammer can spoof that address. However, a white list can be
effective with limited deployment of DKIM if all the people on the
white list are those whose domains are utilizing the mechanism, and
the users on that white list aren't zombies.

This kind of identity mechanism is also applicable to SIP, and is in
fact, exactly what is defined by SIP's authenticated identity
mechanism [17].

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Other signature-based approaches for email include S/MIME[24] and
OpenPGP[25].

5. Authenticated Identity in SIP

One of the key parts of many of the solutions described above is the
ability to securely identify the sender of a SIP message. SIP
provides a secure solution for this problem, called SIP Identity
[17], and it is important to discuss it here.

The solution starts by having each domain authenticate its own users.
SIP provides HTTP digest authentication as part of the core SIP
specification, and all clients and servers are required to support
it. Indeed, digest is widely deployed for SIP. However, digest
alone has many known vulnerabilities, most notably offline dictionary
attacks. These vulnerabilities are all resolved by having each
client maintain a persistent TLS connection to the server. The
client verifies the server identity using TLS, and then authenticates
itself to the server using a digest exchange over TLS. This
technique, which is also documented in RFC 3261, is very secure but
not widely deployed yet. In the long term, this approach will be
necessary for the security properties needed to prevent SIP spam.

Once a domain has authenticated the identity of a user, when it
relays a message from that user to another domain, the sending domain
can assert the identity of the sender, and include a signature to
validate that assertion. This is done using the SIP identity
mechanism [17].

A weaker form of identity assertion is possible using the P-Asserted-
Identity header field [5], but this technique requires mutual trust
among all domains. Unfortunately, this becomes exponentially harder
to provide as the number of interconnected domains grows. As that
happens, the value of the identity assertion becomes equal to the
trustworthiness of the least trustworthy domain. Since spam is a
consequence of the receiving domain not being able to trust the
sending domains to disallow the hosts in the sending to send spam,
the P-Asserted-Identity technique becomes ineffective at exactly the
same levels of interconnectedness that introduce spam.

Consider the following example to help illustrate this fact. A
malicious domain -- let us call them spam.example.com, would like to
send SIP INVITE requests with false P-Asserted-Identity, indicating
users outside of its own domain. spam.example.com finds a regional
SIP provider in a small country who, due to its small size and
disinterest in spam, accepts any P-Asserted-Identity from its
customers without verification. This provider, in turn, connects to
a larger, interconnect provider. They do ask each of their customers

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to verify P-Asserted-Identity but have no easy way of enforcing it.
This provider, in turn, connects to everyone else. As a consequence,
the spam.example.com domain is able to inject calls with a spoofed
caller ID. This request can be directed to any recipient reachable
through the network (presumably everyone due to the large size of the
root provider). There is no way for a recipient to know that this
particular P-Asserted-Identity came from this bad spam.example.com
domain. As the example shows, even though the central provider's
policy is good, the overall effectiveness of P-Asserted-Identity is
still only as good as the policies of the weakest link in the chain.

SIP also defines the usage of TLS between domains, using mutual
authentication, as part of the base specification. This technique
provides a way for one domain to securely determine that it is
talking to a server that is a valid representative of another domain.

6. Framework for Anti-Spam in SIP

Unfortunately, there is no magic bullet for preventing SIP spam, just
as there is none for email spam. However, the combination of several
techniques can provide a framework for dealing with spam in SIP.
This section provides recommendations for network designers in order
to help mitigate the risk of spam.

There are four core recommendations that can be made:

Strong Identity: Firstly, in almost all of the solutions discussed
above, there is a dependency on the ability to authenticate the
sender of a SIP message inter-domain. Consent, reputation
systems, computational puzzles, and payments at risk, amongst
others, all work best when applied only to new requests, and
successful completion of an introduction results in the placement
of a user on a white list. However, usage of white lists depends
on strong identity assertions. Consequently, any network that
interconnects with others should make use of strong SIP identity
as described in RFC 4474. P-Asserted-Identity is not strong
enough.

White Lists: Secondly, with a strong identity system in place,
networks are recommended to make use of white lists. These are
ideally built off existing buddy lists, if present. If not,
separate white lists can be managed for spam. Placement on these
lists can be manual or based on the successful completion of one
or more introduction mechanisms.

Solve the Introduction Problem: This in turn leads to the final
recommendation to be made. Network designers should make use of
one or more mechanisms meant to solve the introduction problem.

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Indeed, it is possible to use more than one and combine the
results through some kind of weight. A user that successfully
completes the introduction mechanism can be automatically added to
the white list. Of course, that can only be done usefully if
their identity is verified by SIP Identity. The set of mechanisms
for solving the introduction problem, as described in this
document, are based on some (but not all) of the techniques known
and used at the time of writing. Providers of SIP services should
keep tabs on solutions in email as they evolve, and utilize the
best of what those techniques have to offer.

Don't Wait Until It's Too Late: But perhaps most importantly,
providers should not ignore the spam problem until it happens! As
soon as a provider inter-connects with other providers, or allows
SIP messages from the open Internet, that provider must consider
how they will deal with spam.

7. Additional Work

Though the above framework serves as a good foundation on which to
deal with spam in SIP, there are gaps, some of which can be addressed
by additional work that has yet to be undertaken.

One of the difficulties with the strong identity techniques is that a
receiver of a SIP request without an authenticated identity cannot
know whether the request lacked such an identity because the
originating domain didn't support it, or because a man-in-the-middle
removed it. As a result, transition mechanisms should be put in
place to allow these to be differentiated. Without it, the value of
the identity mechanism is much reduced.

8. Security Considerations

This document is entirely devoted to issues relating to spam in SIP
and references a variety of security mechanisms in support of that
goal.

9. Acknowledgements

The authors would like to thank Rohan Mahy for providing information
on Habeas, Baruch Sterman for providing costs on VoIP termination
services, and Gonzalo Camarillo and Vijay Gurbani for their reviews.
Useful comments and feedback were provided by Nils Ohlmeir, Tony
Finch, Randy Gellens, Lisa Dusseault, Sam Hartman, Chris Newman, Tim
Polk, Donald Eastlake, and Yakov Shafranovich. Jon Peterson wrote
some of the text in this document and has contributed to the work as
it has moved along.

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10. Informative References

[1] Campbell, B., Mahy, R., and C. Jennings, "The Message Session
Relay Protocol (MSRP)", RFC 4975, September 2007.

[2] Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston, A.,
Peterson, J., Sparks, R., Handley, M., and E. Schooler, "SIP:
Session Initiation Protocol", RFC 3261, June 2002.

[3] Campbell, B., Rosenberg, J., Schulzrinne, H., Huitema, C., and
D. Gurle, "Session Initiation Protocol (SIP) Extension for
Instant Messaging", RFC 3428, December 2002.

[4] Roach, A., "Session Initiation Protocol (SIP)-Specific Event
Notification", RFC 3265, June 2002.

[5] Jennings, C., Peterson, J., and M. Watson, "Private Extensions
to the Session Initiation Protocol (SIP) for Asserted Identity
within Trusted Networks", RFC 3325, November 2002.

[6] Rosenberg, J., "A Presence Event Package for the Session
Initiation Protocol (SIP)", RFC 3856, August 2004.

[7] Rosenberg, J., "A Watcher Information Event Template-Package
for the Session Initiation Protocol (SIP)", RFC 3857,
August 2004.

[8] Rosenberg, J., "An Extensible Markup Language (XML) Based
Format for Watcher Information", RFC 3858, August 2004.

[9] Faltstrom, P. and M. Mealling, "The E.164 to Uniform Resource
Identifiers (URI) Dynamic Delegation Discovery System (DDDS)
Application (ENUM)", RFC 3761, April 2004.

[10] Rosenberg, J., "The Extensible Markup Language (XML)
Configuration Access Protocol (XCAP)", RFC 4825, May 2007.

[11] Rosenberg, J., "Presence Authorization Rules", RFC 5025,
October 2007.

[12] Rosenberg, J., "A Framework for Consent-Based Communications in
the Session Initiation Protocol (SIP)", Work in Progress,
October 2007.

[13] Camarillo, G., "A Document Format for Requesting Consent", Work
in Progress, October 2007.

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[14] Rosenberg, J., "A Framework for Application Interaction in the
Session Initiation Protocol (SIP)", Work in Progress,
October 2005.

[15] Burger, E. and M. Dolly, "A Session Initiation Protocol (SIP)
Event Package for Key Press Stimulus (KPML)", RFC 4730,
November 2006.

[16] Rosenberg, J., "Applying Loose Routing to Session Initiation
Protocol (SIP) User Agents (UA)", Work in Progress, June 2007.

[17] Peterson, J. and C. Jennings, "Enhancements for Authenticated
Identity Management in the Session Initiation Protocol (SIP)",
RFC 4474, August 2006.

[18] Allman, E. and H. Katz, "SMTP Service Extension for Indicating
the Responsible Submitter of an E-Mail Message", RFC 4405,
April 2006.

[19] Lyon, J. and M. Wong, "Sender ID: Authenticating E-Mail",
RFC 4406, April 2006.

[20] Lyon, J., "Purported Responsible Address in E-Mail Messages",
RFC 4407, April 2006.

[21] Wong, M. and W. Schlitt, "Sender Policy Framework (SPF) for
Authorizing Use of Domains in E-Mail, Version 1", RFC 4408,
April 2006.

[22] Dierks, T. and E. Rescorla, "The Transport Layer Security (TLS)
Protocol Version 1.1", RFC 4346, April 2006.

[23] Allman, E., Callas, J., Delany, M., Libbey, M., Fenton, J., and
M. Thomas, "DomainKeys Identified Mail (DKIM) Signatures",
RFC 4871, May 2007.

[24] Ramsdell, B., "Secure/Multipurpose Internet Mail Extensions
(S/MIME) Version 3.1 Message Specification", RFC 3851,
July 2004.

[25] Elkins, M., Del Torto, D., Levien, R., and T. Roessler, "MIME
Security with OpenPGP", RFC 3156, August 2001.

[26] Abadi, M., Burrows, M., Manasse, M., and T. Wobber, "Moderately
Hard, Memory Bound Functions, NDSS 2003", February 2003.

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RFC 5039 SIP Spam January 2008

[27] Abadi, M., Burrows, M., Birrell, A., Dabek, F., and T. Wobber,
"Bankable Postage for Network Services, Proceedings of the 8th
Asian Computing Science Conference, Mumbai, India",
December 2003.

[28] Clayton, R. and B. Laurie, "Proof of Work Proves not to Work,
Third Annual Workshop on Economics and Information Security",
May 2004.

Authors' Addresses

Jonathan Rosenberg
Cisco
Edison, NJ
US

EMail: [email protected]
URI: http://www.jdrosen.net

Cullen Jennings
Cisco
170 West Tasman Dr.
San Jose, CA 95134
US

Phone: +1 408 421-9990
EMail: [email protected]

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RFC 5039 SIP Spam January 2008

Full Copyright Statement

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