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RUCUS Exploratory Working Group                                  D. Wing
Internet-Draft                                                     Cisco
Intended status:  Experimental                              S. Niccolini
Expires:  August 26, 2008                                 M. Stiemerling
                                                           H. Tschofenig
                                                  Nokia Siemens Networks
                                                       February 23, 2008

                           Spam Score for SIP

Status of this Memo

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   This document defines a mechanism for SIP proxies to communicate a
   spam score to downstream SIP proxies and to SIP user agents.  This
   information can then be used as input to other decision making
   engines, for example, to provide alternate call routing or call

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  3
   3.  Calculation of the Spam Score  . . . . . . . . . . . . . . . .  3
   4.  Information passed downstream  . . . . . . . . . . . . . . . .  4
   5.  Operation of Spam-Scoring Proxy  . . . . . . . . . . . . . . .  5
   6.  Operation of Downtream Proxy or User Agent . . . . . . . . . .  6
   7.  Grammar  . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
   8.  Examples . . . . . . . . . . . . . . . . . . . . . . . . . . .  8
   9.  Security Considerations  . . . . . . . . . . . . . . . . . . .  8
   10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . .  9
   11. IANA Considerations  . . . . . . . . . . . . . . . . . . . . .  9
   12. References . . . . . . . . . . . . . . . . . . . . . . . . . .  9
     12.1.  Normative References  . . . . . . . . . . . . . . . . . .  9
     12.2.  Informational References  . . . . . . . . . . . . . . . .  9
   Appendix A.  Changes . . . . . . . . . . . . . . . . . . . . . . . 10
     A.1.   Changes from -00 to -01 . . . . . . . . . . . . . . . . . 10
     A.2.   Changes from -01 to -02 . . . . . . . . . . . . . . . . . 10
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 10
   Intellectual Property and Copyright Statements . . . . . . . . . . 12

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1.  Introduction

   It is desirable for SIP proxies to insert a spam score so that
   downstream SIP proxies and downstream SIP user agents can use a high
   score to decide that special handling is required.  For example, a
   score above 20 might cause one of the spam avoidance techniques
   described in [RFC5039] to be triggered for this call.

   This specification allows each SIP proxy to contribute spam scoring
   information that can be useful to downstream SIP proxies and the SIP
   user agent (UA).  The downstream SIP proxies or SIP UA might ignore
   that information (e.g., it doesn't trust the SIP proxy that generated
   the spam score) or might use it.

   Note that this document does not make the attempt to define how the
   spam score was derived nor to distribute information that could be
   used to verify the spam score generation.  Furthermore, this document
   does not attempt to cryptographically bind the identity of the entity
   generating the score to the value itself.  Hence, its usage is likely
   to be useful only between neighboring administrative domains
   communicating such a score.

   One may wonder why bother marking a message that appears to be SPAM
   when the same process that detected the SPAM can also automatically
   block it.  The answer is that contractual as well as regulatory
   issues may prevent blocking and in these cases while not able to
   block, the detecting proxy can nonetheless notify downstream elements
   of the potential threat.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

3.  Calculation of the Spam Score

   A SIP proxy evaluates an incoming SIP request and generates a spam
   score using a local mechanism.  In order to allow for whitelisting as
   well as blacklisting the scoring is between 0 and 100, 0 indicating
   absolute acceptance (e.g., whitelist), 100 indicating absolute SPAM
   (e.g., blacklist) and scores between 0 and 100 can be considered to
   represent the percentage likelyhood of spam.

   The actual calculation is governed by algorithms (one example is
   found in examples section below) which MAY be agreed upon by the

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   upstream and downstrem domains.  The algorithm MAY be conveyed by the
   downstream doamins to the upstream one out of band prior to the
   upstream domain marking a message for transport downstream.
   Alternatively, a default algorithm can be used if no alternate
   algorithm was established a-priori between upstream and downstream
   domains.  The mechanism for conveyance of algorithm to upstream
   domain is out of scope for this document but can be seen as an
   extension to [I-D.tschofenig-sipping-framework-spit-reduction].

4.  Information passed downstream

   In addition to the score the following other pieces of information
   should be passed downstream as well:

      Realm - Indicating the upstream domain or realm making the claim

      Algorithm - The name of the agreed upon algorithm

      Strength - An integer indicating the confidence of the score

      Info - A text field containing any arbitrary information

      Param[1..3] - 3 general purpose parameters for futureproofing

      IsSpam - A boolean for convenience purposes alone.

   The Relam is shared by all proxies in a domain and enables the
   downstream proxy to determine whether or not the score is to be

   The Algorithm is the name of the algorithm used and can be
   standardized in the same way encryption algorithm names (e.g. sha-1)
   have been standardized.

   The Strength field inidicates the confidence level of the Score.
   This is generated by the same entity which generates the Score, and
   is an output from the algorithim, which is based on a number of
   factors, including for example volume of calls, call type etc.  It is
   meant to quantify the score.  A calling party that makes many calls
   and has an average score of 85 is preferable to calling party who
   made 1 or 2 calls only and has an average score of 95.

   Info is a text field (which can be limited to 64 bytes if we are
   concerned about the MTU) that is there to provide more complete
   information on the SPIT scoring etc, and can be used for diagnosis
   etc. (which is useful for off line analysis reporting etc.).  For

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   example, it could provide an explanation of the score such as is done
   in one particular email spam package.

   In this email example the threshold for SPAM is 7 and this message
   scored a 9.5.  The sample text info below could be used to explain
   how the score was calculated.

         points  rule-name          rule-description
         ------  ----------------   ------------------------------
          2.5    SUBJ_CAPS          Subject lind is capitalized
          1.8    INLINE_GIF         inline GIF detected in message
          3.0    NON_PREF_LANG      Non default language
          2.0    HONEYPOT           Honeypot address in cc
          0.2    KEYWORD_MATCH      suspicious words in message

                     Figure 1: Email Spam Rule example

   As expected in this example, different tests have different scores
   depending on their contribution to the potential of SPAM.  Similarly
   in the case of SPIT there can be many rules applied and having this
   info can enable the receiving party to analyze the results (non

   Params 1,2 and 3 are just general purpose placeholders (containing
   Param_Desc, Param_Value pairs) for future proofing capabilities.
   Since so much is still unknown about IP communications SPAM this is
   seen as a wise approach.

   Finally, as an option it may be wise to have a boolean yes/no kind of
   indicator for all those downstream who do not care to know why the
   upstream element assumes it is spit only that it is.

5.  Operation of Spam-Scoring Proxy

   A SIP proxy evaluates an incoming SIP request and generates a spam
   score using a local mechanism.  This score is between 0 (indicating
   the message is not spam) and 100 (indicating the message is spam).
   Values between 0 and 100 indicate the 'likelihood' that the SIP
   request is spam, with higher values indicating a higher likelihood
   the message is spam.

   This spam score is inserted into the new "Spam-Score" header.  This
   header field contains a summary spam score and optionally contains
   detail information.  The detail information is implementation
   dependent.  The detail information is valuable for debugging and to
   provide the SIP user agent or SIP proxy with additional information
   regarding how the spam-scoring SIP proxy's local mechanism arrived at

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   the summary spam score.

6.  Operation of Downtream Proxy or User Agent

   A downstream proxy or the SIP user agent MAY use the spam score or
   spam-detail information to change call routing or call handling.  It
   is envisioned that some form of policies indicate the trusted proxies
   in order to decide which spam scores to consider for special call

      In some jurisdictions, the end user needs to authorize call
      handling, including rejection of a call based on a spam score.
      Mechanisms to allow users to authorize such policies are, however,
      out of scope of this document.

   The behavior of the SIP proxy or user agent when the spam score is
   above a certain value is a local policy matter.  Examples of behavior

   o  a SIP request with a high spam score might cause a proxy or user
      agent to redirect the SIP request to company's main telephone
      extension or to the user's voicemail

   o  a user agent might alert the user by flashing the phone (without
      audible ringing)

   o  a user agent might allow calls with a spam score below a certain
      value during daylight hours, but deny such calls at night.

   o  a proxy might challenge the caller to complete a Turing test.

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7.  Grammar

   ABNF using the ABNF syntax of [RFC3261]:

     extension-header   = "Spam-Score:" SP
                          spam-score *[ SP ";" spam-detail ]

     spam-score         = score SP "by" SP hostname
     score              = 1*3DIGIT [ "." 1*3DIGIT ]

     spam-detail    = spam-strength / spam-algorithm / spam-param

     spam-algorithm = "spam-algorithm" EQUAL quoted-string

     spam-strength  = "spam-score-strength" EQUAL strength
     strength       = 1*3DIGIT [ "." 0*3DIGIT ]

     spam-info      = "spam-info" EQUAL info-value
     info-value     = quoted-string

     spam-param1    = "spam-param1" EQUAL param-value
     param-value    = quoted-string

     spam-param2    = "spam-param2" EQUAL param-value
     param-value    = quoted-string

     spam-param3    = "spam-param3" EQUAL param-value
     param-value    = quoted-string

     spam-isspam    = [ "isSpam" ]

                              Figure 2: ABNF

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8.  Examples

   The following example shows a SIP score generated and inserted by two
   SIP proxies, sip.example.com and sip.example.net.  In this example,
   sip.example.com is owned by a spammer who is trying to fool
   downstream systems with their low spam score (0).  However, the
   example.net proxies and user agents only pay attention to spam scores
   from Spam-Score headers generated by example.net proxies, so
   example.com's attempts to fool the downstream proxies (with its low
   spam score) are in vain.  Note also the sample Session Duration Time
   (SDT) algorithm SDT [I-D.malas-performance-metrics] simply compares
   the given callers previous session duration time with the expected
   session duration time over all destinations.

     INVITE sip:bob@example.net SIP/2.0
     Via: SIP/2.0/UDP sip.example.net;branch=z9hG4bKnashds8
     Spam-Score: 75 by sip.example.net
       ;spam-info="High call volume"
     Via: SIP/2.0/UDP sip.example.com;branch=z9hG4bKfjzc
     Max-Forwards: 70
     To: Bob <sip:bob@example.net>
     From: Alice <sip:alice@example.com>;tag=1928301774
     Call-ID: a84b4c76e66710@pc33.example.com
     CSeq: 314159 INVITE
     Contact: <sip:alice@pc33.example.com>
     Content-Type: application/sdp
     Content-Length: 142

     [... SDP elided from this example...]

                    Figure 3: Example with spam scores

9.  Security Considerations

   SIP proxies and SIP user agents need to ignore spam scores generated
   by proxies that aren't trusted.  As the spam scores are inserted
   along with Via:  headers, the last Via header inserted by a trusted
   proxy indicates the last trusted spam score.

   In addition, the entire issue of securing the channel between the
   upstream and downstream domains MUST be addressed via mechanisms such

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   as TLS.

10.  Acknowledgements

   Thanks to Joachim Charzinski, Daniel Quinlan, and S. Moonesamy for
   their suggestions to improve this document.  Thanks to David Schwartz
   for his contributed text and to Eli Katz for editing assistance.

11.  IANA Considerations

   [[This section will be completed in a later version of this

12.  References

12.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

   [RFC3261]  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.

12.2.  Informational References

   [RFC5039]  Rosenberg, J. and C. Jennings, "The Session Initiation
              Protocol (SIP) and Spam", RFC 5039, January 2008.

              Tschofenig, H., Schulzrinne, H., Wing, D., Rosenberg, J.,
              and D. Schwartz, "A Framework to tackle Spam and Unwanted
              Communication for Internet  Telephony",
              draft-tschofenig-sipping-framework-spit-reduction-02 (work
              in progress), November 2007.

              Malas, D., "SIP End-to-End Performance Metrics",
              draft-malas-performance-metrics-08 (work in progress),
              December 2007.

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Appendix A.  Changes

   Note to RFC Editor:  please remove this section prior to publication.

A.1.  Changes from -00 to -01

   o  Changed scoring from positive/negative to 0-100 range.

   o  Moved score from a "Via:" extension to a new header "Spam-Score:".

   o  Changed from Standards Track to Experimental.

A.2.  Changes from -01 to -02

   o  Describe how spam score could be computed

   o  Added more descripive text describing how the header is passed
      downstream towards the user agent

Authors' Addresses

   Dan Wing
   Cisco Systems, Inc.
   170 West Tasman Drive
   San Jose, CA  95134

   Email:  dwing@cisco.com

   Saverio Niccolini
   Network Laboratories, NEC Europe Ltd.
   Kurfuersten-Anlage 36
   Heidelberg  69115

   Phone:  +49 (0) 6221 4342 118
   Email:  saverio.niccolini@netlab.nec.de
   URI:    http://www.netlab.nec.de

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   Martin Stiemerling
   Network Laboratories, NEC Europe Ltd.
   Kurfuersten-Anlage 36
   Heidelberg  69115

   Phone:  +49 (0) 6221 4342 113
   Email:  stiemerling@netlab.nec.de
   URI:    http://www.netlab.nec.de

   Hannes Tschofenig
   Nokia Siemens Networks
   Linnoitustie 6
   Espoo  02600

   Phone:  +358 (50) 4871445
   Email:  Hannes.Tschofenig@nsn.com
   URI:    http://www.tschofenig.com

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