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Versions: (draft-rosenberg-sipping-spam) 00 01 02 03 04 05 RFC 5039

SIPPING                                                     J. Rosenberg
Internet-Draft                                               C. Jennings
Expires: September 7, 2006                                         Cisco
                                                             J. Peterson
                                                                 Neustar
                                                           March 6, 2006


             The Session Initiation Protocol (SIP) and Spam
                       draft-ietf-sipping-spam-02

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Copyright Notice

   Copyright (C) The Internet Society (2006).

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



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   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.  Discussions on this
   draft should be directed at sipping@ietf.org.

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  . . . . . . . . . . . . . . . . . . . . . . .   8
     3.3  White Lists  . . . . . . . . . . . . . . . . . . . . . . .   9
     3.4  Consent-Based Communications . . . . . . . . . . . . . . .  10
     3.5  Reputation Systems . . . . . . . . . . . . . . . . . . . .  11
     3.6  Address Obfuscation  . . . . . . . . . . . . . . . . . . .  13
     3.7  Limited Use Addresses  . . . . . . . . . . . . . . . . . .  14
     3.8  Turing Tests . . . . . . . . . . . . . . . . . . . . . . .  14
     3.9  Computational Puzzles  . . . . . . . . . . . . . . . . . .  16
     3.10   Payments at Risk . . . . . . . . . . . . . . . . . . . .  16
     3.11   Legal Action . . . . . . . . . . . . . . . . . . . . . .  17
     3.12   Circles of Trust . . . . . . . . . . . . . . . . . . . .  18
     3.13   Centralized SIP Providers  . . . . . . . . . . . . . . .  18
     3.14   Sender Checks  . . . . . . . . . . . . . . . . . . . . .  19
   4.   Authenticated Identity in SIP  . . . . . . . . . . . . . . .  20
   5.   Framework for Anti-Spam in SIP . . . . . . . . . . . . . . .  21
   6.   Additional Work  . . . . . . . . . . . . . . . . . . . . . .  22
   7.   Security Considerations  . . . . . . . . . . . . . . . . . .  22
   8.   Acknowledgements . . . . . . . . . . . . . . . . . . . . . .  22
   9.   Informative References . . . . . . . . . . . . . . . . . . .  22
        Authors' Addresses . . . . . . . . . . . . . . . . . . . . .  24
        Intellectual Property and Copyright Statements . . . . . . .  26















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1.  Introduction

   Spam, defined as the transmission of bulk unsolicited email, has been
   a plague on the Internet email system, rendering it nearly useless.
   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.  Although it has seen widespread deployment,
   the deployments today have mostly been in disconnected islands.
   Providers have not yet connected to each other in significant ways,
   nor have they yet opened up access so as to allow receipt of SIP
   messaging from the open Internet.  Possibly as a result of this, SIP
   networks have not yet been the target of any significant amount of
   spam.  However, we believe that it is just a matter of time.

   It is important that the SIP community react now, rather than later,
   and define and deploy anti-spam measures before the problem arises.
   This document serves to help frame the problem of spam in SIP and
   analyze the solution space in order to help determine a path forward.

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.

   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 which 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.



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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 [7]), 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.

   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 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 which 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 UAC 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 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 10s 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 10s of content.

   With SIP, this cost is much reduced.  Consider a spammer using a
   typical broadband Internet connection that provides 500Kbps of
   upstream bandwidth.  Initiating a call requires just a single INVITE
   message.  Assuming, for simplicity's sake, that this is 1kB, a
   500Kbps 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 5.6 Kbps), as many as 90
   simultaneous calls can be in progress.  With 10s of content per call,
   that allows for 9 successful call attempts per second.  This means
   that a system could deliver a voice message successfully to users at
   a rate of around 9 per second.  If broadband access is around $50/
   month, the cost per successful voice spam is about 215 microcents
   each.  This assumes that calls can be made 24 hours a day, which may
   or may not be the case.

   These figures indicate that SIP call spam is roughly three orders of
   magntiude 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 is very little
   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 URL are possible from anywhere in the world.  This will allow
   for international spam at a significantly reduced cost.
   International spam is likely to be even more annoying that national
   spam, since it may arrive in languages that the recipient doesn't



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   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 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 (there are probably around 5 million
   addressable SIP endpoints on the Internet as of writing), 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.

   This number (9 deliveries per second) is below the successful message
   delivery rate of email [[NOTE: is there a figure for this]].
   However, many spam messages are automatically deleted by filters or
   users without ever being read.  It is far more likely that a call
   spam will be examined by a user if its delivered, due to the
   difficulty in automated content filtering (see below).  Thus, when
   one examines the final figure of importance - the number of new
   customers attracted per spam delivered, it is far from clear whether
   call spam or email spam will be more effective.

   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 that, 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, thats 62
   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, many IM systems employ
   white lists, which only allow spam 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 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.  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 [8].  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:please-buy-my-product@spam.example.com, 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
   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.



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   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.  These 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 is almost 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.

   Therefore, our conclusion is that the most successful form of anti-
   spam measures used in email are almost useless for call spam.

   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.

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 (spammer@domain.com) and entire domains (spammers.com).



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   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, black
   lists will have limited effectiveness for email.

   Blacklists are also likely to be ineffective for SIP spam.
   Fortunately, SIP has much stronger mechanisms for inter-domain
   authenticated identity than email has (see Section 4).  Assuming
   these mechanisms are used and enabled in inter-domain communications,
   it becomes nearly impossible 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 can not 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 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.

   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.  IM systems also have strong identity mechanisms



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   due to their closed nature.  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, more so than email.  This is because SIP also provides a buddy
   list concept and has an advanced presence system as part of its
   specifications.  Second, unlike email, but like IM systems, SIP can
   provide a much more secure form of authenticated identity, even for
   inter-domain communications.  As a result, the problem of forged
   senders can be eliminated, making the white list solution feasible.

   The introduction problem remains, however.  In email, techniques like
   the Turing tests have been employed for this purpose.  Those 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.

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 but not in email.  This is likely due to the need for a secure
   authenticated identity mechanism, which is a pre-requisite for this
   kind of solution.  Since most of today's IM systems are closed,
   sender identities can be authenticated.

   This kind of consent-based communications has been standardized in
   SIP for presence, using the watcher information event package [8] and
   data format [9], which allow a user to find out that someone has
   subscribed.  Then, the XML Configuration Access Protocol (XCAP) [11]
   is used, along with the XML format for presence authorization [12] to
   provide permission for the user to communicate.  However, to date,
   these techniques have been applied strictly for presence.

   If they were extended to cover IM and calling, would it help?  It is
   hard to say.  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



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   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 freeform text, and thus
   could be used to convey brief messages.  One can imagine receiving
   consent requests with identities like,
   "sip:please-buy-my-product-at-this-website@spam.example.com", for
   example.  Fortunately, traditional content-based filtering can be
   applied to this type of information.

   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, and nearly all users do not give consent
   to spammers, the value in sending spam is reduced, and perhaps it
   will cease.

   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:bob@example.com 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 make the permission is extremely limited.  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.  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 they
   attempt to contact user B. If a consent-based system is used, B is
   prompted to consent to communications from A, a reputation score
   might be displayed in order to help B decide whether or not they
   should accept communications from A.



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   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 not enjoyed much success outside of the
   instant messaging space.  This is in part because few other
   communications systems admit of the same degree of centralization and
   monolithic control.  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
   negative reputation will simply acquire a new one.  Moreover,
   negative reputation is generated by tattling, which requires users to
   be annoyed enough to click the warning button.  Additionally, it can
   be abused.  In some reputation systems, "reputation mafias"
   consisting of large numbers of users routinely bully or extort
   victims by threatening collectively to grant 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 artifical 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 require a new
   identity, since basing authorization decisions on positive reputation
   is essentially a form of whitelisting.

   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



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   effect, a white list - and one can therefore probably 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 buddylists would not affect
   your perception of their reputation unless their circle linked to
   your own social network.

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
   prevent 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 websites 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 publically accessible service containing addresses will
   suffice.  As a result, ENUM [10] 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.






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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.  They give out different email addresses to different
   people.  Once spam begins arriving at an address, the user terminates
   the address and replaces it with another.

   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 [7].
   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
   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.

3.8  Turing Tests

   In email, Turing tests are those solutions whereby the sender of the
   message is given some kind of puzzle or challenge, which only a human
   can answer.  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.  Automata cannot easily perform the image
   recognition needed to extract the word or number sequence, but a
   human user usually can.  Since Turing tests rely on video or audio
   puzzles, they sometimes cannot be solved by individuals with
   handicaps.

   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.  Indeed, the presence strong
   authenticated identity techniques in SIP will make such a Turing test
   approach more effective in SIP than in email.

   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 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 whitelist.

   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, there are problems with the Turing test
   described above.  First, it is language specific.  The application
   could be made to run in different languages, if the caller indicates
   their supported languages.  This is possible in SIP, using the
   Accept-Language header field, but this is not widely used at the
   moment.

   The other problem with this 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 $100 US dollars per year, and
   assuming a Turing test of 30 second duration, this ends up being
   about ten thousand messages per dollar, or about 10,000 microcents
   per message.  Though much more expensive than the 31 microcents per
   message to send an IM spam, it is still relatively inexpensive.

   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 [19].  It seems that there is less variety in the CPU/memory
   bandwidth across devices, roughly a single order of magnitude.

   Recent work [21] 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.
   However, if combined with white listing, the computational puzzles
   only become needed for validating new communication partners.  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.  Of course, it is
   likely that these techniques will come with a lot of patents and
   other intellectual property constraints.

3.10  Payments at Risk

   This approach has been proposed for email [20].  When user A sends 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



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   be received in order to avoid reuse of "pending payments" across
   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 truly make it prohibitively
   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, for
   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 .75 cents and deliver
   4.25 cents.  If the sender is allowed, the recipient returns 4.25
   cents, the provider takes 64 cents, and returns 3.6 cents.  This
   costs the sender .65 cents on each transaction, if it was legitimate.
   If there are ten new recipients per day, thats US $1.95 per month,
   which is relatively inexpensive.

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.  Whether they are or are not effective,
   they would appear to be equally effective (or ineffective, as the
   case may be) in preventing SIP 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
   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



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   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 a spammer inserted it without an
   appropriate license, allegedly causes the spammer to be violating 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
   TLS between providers.  Email does not have this kind of secure
   domain identification, although new techniques are being investigated
   to add it using reverse DNS checks (see below).

   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?  Would a very large enterprise 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

   In this technique, 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 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.



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   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
   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, it puts back into SIP much of the
   complexity and monopolistic structures that SIP promised to
   eliminate.  As such, it is a solution that the authors find
   distasteful and contrary to the SIP design and architecture.

3.14  Sender Checks

   In email, there has been a lot of interest in defining new DNS
   resource records that will allow a domain that receives a message to
   verify that the sender is a valid MTA for the sending domain [18]
   [16].

   Are these techniques useful for SIP?  They can be used for SIP but
   are not necessary.  In email, there are no standards established for
   securely identifying the identity of the sending domain of a message.
   In SIP, however, 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



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   of RFC 3261 [2].  These techinques, however, are only applicable in
   the trapezoid model where there is a sending and a receiving domain
   only.  In forwarding situations, the assumption no longer holds and
   these techniques no longer work.

   Thus, instead of creating DNS entries containing the IP address of
   each legitimate relay for a domain, the provider can give each
   legitimate relay a certificate that allows them to authenticate
   themselves as coming from that domain.  Such a technique would work
   even in the face of IP address spoofing, which the marid techniques
   are susceptible to.

4.  Authenticated Identity in SIP

   One of the key parts of many of the solutions described above is the
   ability to securely identify the identity of a sender of a SIP
   message.  SIP provides a secure solution for this problem, 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 [6], but this technique requires mutual trust
   among all domains.  Unfortunately, this becomes expontentially 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 untrusted domains and users that get connected to the
   network, the P-Asserted-Identity technique becomes ineffective at
   exactly the same levels of interconnectness that introduce spam.




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   A further weakness of P-Asserted-ID is that the actual domain which
   asserted the identity cannot be known.  If that domain could be
   reliably known, then its assertions could be tempered based on user
   or domain-wide policiies.  This weakness is not present in [17],
   which allows the recipient of a message to cryptographically
   determine the identity of the asserting domain.

   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.

5.  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.

   Strong Authenticated Identity is Key: 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.  As such,
      we would argue that any provider that performs inter-domain SIP
      messaging must use the techniques described in Section 4, and in
      particular, depend on the strong identity techniques in [17].

   Whitelists: With a strong identity mechanism in place, whitelists can
      facilitate communications from known callers.  That reduces the
      scope of the problem to the introduction problem.

   Consent Framework: The SIP consent framework [13] extends the
      presence framework for consent to all communications.  Consent
      plays an important role in helping address the introduction
      problem.

   Leverage What Email has to Offer: With the consent framework in
      place, spammers have only a small window through which they can
      introduce content to recipients.  Fortunately, that problem is
      similar to traditional email spam, and can be addressed using the
      various email-based anti-spam techniques.  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.  But
      perhaps most importantly, providers should not ignore the spam
      problem until it happens!  That is the pitfall email fell into.
      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.





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6.  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.

   The consent framework depends on the ability for users to make a
   determination about whether to grant consent for unknown senders.  In
   order for that framework to be useful, it needs to be coupled with
   techniques to ascertain trustworthiness.  Reputation systems, for
   example, can help with that.  At this time, reputation systems have
   seen implementation only within single domains, and using proprietary
   techniques.  A standards-based inter-domain solution would be a
   valuable part of this framework.

7.  Security Considerations

   This memo is entirely devoted to issues relating to secure usage of
   SIP services on the Internet.

8.  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 for his review.  Useful comments and
   feedback were provided by Nils Ohlmeir, Tony Finch, Randy Gellens and
   Yakov Shafranovich.

9.  Informative References

   [1]   Campbell, B., "The Message Session Relay Protocol",
         draft-ietf-simple-message-sessions-14 (work in progress),
         February 2006.

   [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



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         Instant Messaging", RFC 3428, December 2002.

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

   [5]   Peterson, J., "A Privacy Mechanism for the Session Initiation
         Protocol (SIP)", RFC 3323, November 2002.

   [6]   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.

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

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

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

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

   [11]  Rosenberg, J., "The Extensible Markup Language (XML)
         Configuration Access Protocol (XCAP)",
         draft-ietf-simple-xcap-08 (work in progress), October 2005.

   [12]  Rosenberg, J., "Presence Authorization Rules",
         draft-ietf-simple-presence-rules-04 (work in progress),
         October 2005.

   [13]  Rosenberg, J., "A Framework for Consent-Based Communications in
         the Session Initiation  Protocol (SIP)",
         draft-ietf-sipping-consent-framework-04 (work in progress),
         March 2006.

   [14]  Rosenberg, J., "A Framework for Application Interaction in the
         Session Initiation Protocol  (SIP)",
         draft-ietf-sipping-app-interaction-framework-05 (work in
         progress), July 2005.

   [15]  Burger, E., "A Session Initiation Protocol (SIP) Event Package
         for Key Press Stimulus  (KPML)", draft-ietf-sipping-kpml-07
         (work in progress), December 2004.




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   [16]  Lyon, J., "Sender ID: Authenticating E-Mail",
         draft-ietf-marid-core-03 (work in progress), August 2004.

   [17]  Peterson, J. and C. Jennings, "Enhancements for Authenticated
         Identity Management in the Session Initiation  Protocol (SIP)",
         draft-ietf-sip-identity-06 (work in progress), October 2005.

   [18]  Danisch, H., "The RMX DNS RR and method for lightweight SMTP
         sender authorization", draft-danisch-dns-rr-smtp-04 (work in
         progress), May 2004.

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

   [20]  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.

   [21]  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
   600 Lanidex Plaza
   Parsippany, NJ  07054
   US

   Phone: +1 973 952-5000
   Email: jdrosen@cisco.com
   URI:   http://www.jdrosen.net


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

   Phone: +1 408 527-9132
   Email: fluffy@cisco.com






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   Jon Peterson
   Neustar
   1800 Sutter Street
   Suite 570
   Concord, CA  94520
   US

   Phone: +1 925 363-8720
   Email: jon.peterson@neustar.biz
   URI:   http://www.neustar.biz









































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