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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 handling.
3. Calculation of the Spam Score
4. Information passed downstream
5. Operation of Spam-Scoring Proxy
6. Operation of Downtream Proxy or User Agent
9. Security Considerations
11. IANA Considerations
12.1. Normative References
12.2. Informational References
Appendix A. Changes
A.1. Changes from -00 to -01
A.2. Changes from -01 to -02
§ Authors' Addresses
§ Intellectual Property and Copyright Statements
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] (Rosenberg, J. and C. Jennings, “The Session Initiation Protocol (SIP) and Spam,” January 2008.) 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.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119] (Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” March 1997.).
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 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] (Tschofenig, H., Schulzrinne, H., Wing, D., Rosenberg, J., and D. Schwartz, “A Framework to tackle Spam and Unwanted Communication for Internet Telephony,” July 2008.).
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 (0-100)
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 trusted.
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 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 realtime)
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.
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 the summary spam score.
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 treatment.
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 include:
ABNF using the ABNF syntax of [RFC3261] (Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston, A., Peterson, J., Sparks, R., Handley, M., and E. Schooler, “SIP: Session Initiation Protocol,” June 2002.):
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 |
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 (Malas, D., “SIP End-to-End Performance Metrics,” December 2007.) [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:firstname.lastname@example.org SIP/2.0 Via: SIP/2.0/UDP sip.example.net;branch=z9hG4bKnashds8 ;received=192.0.2.1 Spam-Score: 75 by sip.example.net ;detail="SIPfilter-1.0;call_volume=75" ;spam-algorithm="SDT" ;spam-score-strength=50 ;spam-info="High call volume" ;spam-isSpam Via: SIP/2.0/UDP sip.example.com;branch=z9hG4bKfjzc ;received=192.0.2.127 Max-Forwards: 70 To: Bob <sip:email@example.com> From: Alice <sip:firstname.lastname@example.org>;tag=1928301774 Call-ID: email@example.com CSeq: 314159 INVITE Contact: <sip:firstname.lastname@example.org> Content-Type: application/sdp Content-Length: 142 [... SDP elided from this example...]
| Figure 3: Example with spam scores |
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 as TLS.
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.
[[This section will be completed in a later version of this document.]]
|[RFC2119]||Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels,” BCP 14, RFC 2119, March 1997 (TXT, HTML, XML).|
|[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 (TXT).|
|[RFC5039]||Rosenberg, J. and C. Jennings, “The Session Initiation Protocol (SIP) and Spam,” RFC 5039, January 2008 (TXT).|
|[I-D.tschofenig-sipping-framework-spit-reduction]||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-04 (work in progress), July 2008 (TXT).|
|[I-D.malas-performance-metrics]||Malas, D., “SIP End-to-End Performance Metrics,” draft-malas-performance-metrics-08 (work in progress), December 2007 (TXT).|
Note to RFC Editor: please remove this section prior to publication.
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|170 West Tasman Drive|
|San Jose, CA 95134|
|Network Laboratories, NEC Europe Ltd.|
|Phone:||+49 (0) 6221 4342 118|
|Network Laboratories, NEC Europe Ltd.|
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|Nokia Siemens Networks|
|Phone:||+358 (50) 4871445|
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