Internet DRAFT - draft-ietf-repute-considerations
draft-ietf-repute-considerations
REPUTE M. Kucherawy
Internet-Draft November 20, 2013
Intended status: Informational
Expires: May 24, 2014
Considerations Regarding Third-Party Reputation Services
draft-ietf-repute-considerations-03
Abstract
Reputation services offer quality assessments about likely future
behavior, based on past behaviors. The use of these services has
become a common tool in many applications that seek to apply
collected intelligence about traffic sources. Often this is done
because it is common or even expected operator practice. It is
therefore important to be aware of a number of considerations for
both operators and consumers of the data. This document includes a
collection of the best advice available regarding providers and
consumers of reputation data, based on experience to date. Much of
this is based on experience with email reputation systems, but the
concepts are generally applicable.
Status of This Memo
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This Internet-Draft will expire on May 24, 2014.
Copyright Notice
Copyright (c) 2013 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
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publication of this document. Please review these documents
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Background . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Using Reputation Services . . . . . . . . . . . . . . . . . . . 4
4. Providing Reputation Services . . . . . . . . . . . . . . . . . 6
5. Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
6. Security Considerations . . . . . . . . . . . . . . . . . . . . 8
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 8
8. Informative References . . . . . . . . . . . . . . . . . . . . 8
Appendix A. Acknowledgments . . . . . . . . . . . . . . . . . . . 9
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1. Introduction
Reputation services involve collecting feedback from the community
about sources of Internet traffic and aggregating that feedback into
a rating of some kind. Common examples include feedback about
traffic associated with specific email addresses, URIs or parts of
URIs, IP addresses, etc. The specific collection, analysis, and
rating methods vary from one service to the next and one problem
domain to the next, but several operational concepts appear to be
common to all of these.
The promise of the protection that relying on reputation services
offers can be enticing, and many users and operators alike typically
engage those services merely because it is expected of them. A
critical notion, however, is that use of such a service explicitly
involves a third party in the flow of data being received. This is
often taken for granted, with potentially disastrous results.
This document highlights this and other considerations in providing
and consuming reputation data services.
2. Background
The anti-abuse community has historically focused on identifying
sources that misbehave, i.e., that earn negative reputations. For
email, this means identifying sources of spam; for security, it means
identifying sources of penetration attacks. The purpose here is to
identify and filter traffic from bad actors. This grew out of
operational need. As the Internet grew, so did the occurrence of
problematic traffic, especially in email. The pragmatics of email
(i.e., the fact that the total IP address space is more constrained
than the total email address space) drove the focus on using IP
addresses as the focus of reputation, in addition to the fact that IP
addresses have a degree of validation (via the TCP/IP infrastructure)
where email addresses have had none.
The major considerations around a third-party reputation service are:
Raw data: The method of obtaining the information that will be
analyzed;
Rating method: The techniques used on the collected data to compute
a rating or other expression of expected behavior;
Publication: How consumers obtain the computed ratings.
A specific example of a publication method in common use in the email
space is the DNS blacklist [DNSBL]. In particular, the operator of a
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reputation service computes reputations of IP addresses and stores
them in a database. Via a DNSBL query, a consumer can query the
database as to whether mail should be accepted from a particular
source of incoming [SMTP], based on previous observations and
feedback. The service uses the IP address of the source as the basis
for a query to the database, accessed through the Domain Name System
[DNS]. [DNSBL] includes several points in its Security
Considerations document that are repeated and further developed here.
However, regardless of the identifier used for a reputation, bad
actors can evade detection or its consequences by changing
identifiers (e.g., move to a new IP address, register a new domain
name, use a sub-domain). This makes the problem space effectively
boundless, especially as IPv6 rolls out, with its vastly larger
address space.
A framework for reputation services is introduced in [REPUTE] and the
documents it references.
3. Using Reputation Services
Operators that choose to make use of treputation services to
influence content allowed to pass into or through their
infrastructures need to understand that they are granting a third
party (the reputation service provider, or RSP) the ability to affect
the handling of incoming traffic, for better or worse. Of course,
this is the whole point of engaging an RSP when everything is working
properly, but a number of issues are worthy of consideration before
establishing such a relationship.
Some cases have occurred where an RSP made the unilateral decision to
terminate its service. To encourage its clients to stop issuing
queries, it began reporting a maximally negative reputation about all
subjects, causing rejection of all incoming traffic during the
incident period. Although one would hope such incidents to be rare,
automated means to detect such unfortunate returns (malicious or
otherwise) and take remedial should be considered.
RSPs will be the subject of attacks once it is understood that
success in doing so will allow malicious content to evade detection
and filtering. Users of RSPs need to plan for possible interruptions
in service availability or quality.
Similarly, some actors will try to "game" the service, which is to
say that such actors will attempt to determine patterns of behavior
that result in the reporting of favorable reputations, and in doing
so, acquire artificially inflated reputations. One could reasonably
assume that a reputation service is inherently fragile. For
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operational clients, this should prompt balanced and comparative,
rather than unilateral, use of the service.
It is suggested that, when engaging an RSP, an operator should try to
learn the following things about the RSP in order to understand the
exposure potential:
o the RSP's basis for listing or not listing particular subjects;
o if an RSP is paid by its listees, the rate and criteria for
rejection from being listed;
o how the RSP collects data about subjects;
o how many data points are input to the reported reputation;
o whether reputation is based on a reliable identifier;
o how the RSP establishes reliability and authenticity of those
data;
o how continuing data validity is maintained (e.g., on-going
monitoring of the reported data and sources);
o how actively data validity is tracked (e.g., how changes are
detected);
o how disputed reputations are handled;
o how often input data expire;
o whether older information is more or less influential than newer;
o whether the reported reputation a scalar, a Boolean value, a
collection of values, or something else;
o when transitioning among RSPs, the differences between them among
these above points; that is, whether a particular score from one
means the same thing from another.
An operator using an RSP would be wise to ensure it has the
capability to give preference to local policies, for cases where the
client expects to disagree with the reported reputation.
An operator should be able limit the impact of a negative reputation
on content acceptance. For example, rather than rejecting content
outright when a negative reputation is returned, simply subject it to
additional (i.e., more thorough) local analysis before permitting the
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traffic to pass. In other words, the reputation may simply allow
certain layers of a multi-layered filtering system to be bypassed
when that reputation is favorable.
A sensible default should apply when the RSP is not available. This
can also be a query to a different RSP known to be less robust than
the primary one.
Recent proposals such as the experimental system implemented in
[OPENDKIM] have focused on tailoring operation to prefer or emphasize
content whose sources have positive reputations. See Section 5 for
discussion of this notion. As stated in Section 1, negative
reputations are easy to shed, while the universe of things that will
earn and maintain positive reputations is relatively small.
Designing a filtering system that observes these notions is expected
to be more lightweight to operate and harder to game.
One choice is to query and cross-reference multiple RSPs. This can
help to detect which ones under comparison are reliable, and offsets
the effect of anomalous replies. More generally, a robust mechanism
that is using a third-party service needs to contain an array of
mechanisms, and to limit its dependence on any one mechanism, as well
as protect against for misbehavior by an individual mechanism.
4. Providing Reputation Services
Operators intending to provide a reputation service need to consider
that there are many flavors of clients. There will be clients that
are prepared to make use of a reputation service blindly, while
others will be interested in understanding more fully the nature of
the service being provided. These can be likened to a consumer
credit check that only seeks a yes-or-no reply versus wanting to
review a detailed credit report. An operator of an RSP should be
prepared to answer as many of the questions identified in Section 3
as possible, not only because wise clients will ask, but also because
they reflect issues that have arisen over the years, and diligent
exploration of the points they raise will result in a better
reputation service.
Obviously, in computing reputations via traffic analysis, some
private algorithms may come into play. For some RSPs, such "secret
sauce" comprises their competitive advantage over others in the same
space. This document is not suggesting that all private algorithms
need to be exposed for a reputation service to be acceptable.
Instead, it is anticipated that enough of the above details need to
be available to ensure consumers (and in some cases, industry or the
general public) that the RSP can be trusted to influence key local
policy decisions.
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Reputations should be based on accurate identifiers, i.e., some
property of the content under analysis that is difficult to falsify.
For example, in the realm of email, the address found in the From:
header field of a message is typically not verifiable, while the
domain name found in a validated domain-level signature is. In this
case, constructing a reputation system based on the domain name is
more useful than one based on the From: field.
The biggest frustration with most RSPs to date has been the challenge
of dealing with errors: there ofen is no visible, accessible, and
transparent process for remediating the errant addition of an
identifier to a negative reputation list. An RSP in widespread use
is perceived to have enormous power when its results are used to
reject traffic outright; when a "bad" entry is added referencing a
good actor, it can have destructive effects, so an effective
mechanism to fix such problems needs to exist.
Clients clients with varying sensitivities need to be accomodated.
The mechanism that is used to access the RSP should provide an
ability to request that query results include details about the basis
for producing those results. This will help the user to decide how
to apply those results. For example, it should be possible for the
reply to contain:
o the result itself;
o the number of data points used to compute the result;
o the age range of the data;
o source diversity of the input data;
o currency of the result (i.e., when it was computed);
o basis of the result (i.e., which identifier was used).
The systems and algorithms used by the RSP to compute the reported
reputation will need to be hardened as much as practicable against
gaming or other forms of data poisoning. Larger source diversities
are harder to overcome with poisoned input, but are expensive to
build in terms of both infrastructure and time.
Systems focused on assigning positive reputations rather than
negative ones are promising since positive reputations, if made
difficult to earn, put a large cost on bad actors, which may be
enough to dissuade them entirely.
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5. Evolution
Recent consideration of reputation efforts is evolving toward the
identification of good actors rather than bad actors, and giving them
preferential treatment. This drastically reduces the problem space:
There are vastly more IP addresses and email addresses used by bad
actors to generate problematic traffic than are used by good actors
to generate desirable traffic.
Moreover, good actors tend to be represented by stable names and
addresses, allowing users to rely on these to identify and give
preferential treatment to their traffic. Good actors have no need to
hop around to different addresses, and already work to keep their
traffic clean. In addition, good actors are willing and able to
collaborate in the assessment process, such as by supplying validated
identifiers that are associated with their traffic.
This new approach of focusing on identification of good actors has
only been tried to date using manually edited whitelists, but has
shown promising results on that scale.
6. Security Considerations
Several points are raised above that can be described as threats to
the delivery of valid user data. This document highlights and
discusses those matters, but introduces no new security issues.
7. IANA Considerations
This memo contains no actions for IANA.
[RFC Editor: Please remove this section prior to publication.]
8. Informative References
[DNS] Mockapetris, P., "Domain Names -- Concepts and
Facilities", RFC 1034, November 1987.
[DNSBL] Levine, J., "DNS Blacklists and Whitelists", RFC 5782,
February 2010.
[OPENDKIM] "OpenDKIM (Open Source DKIM)", July 2013,
<http://www.opendkim.org>.
[REPUTE] Borenstein, N. and M. Kucherawy, "An Architecture for
Reputation Reporting", RFC 7070, November 2013.
[SMTP] Klensin, J., "Simple Mail Transfer Protocol", RFC 5321,
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October 2008.
Appendix A. Acknowledgments
The author wishes to acknowledge the following for their review and
constructive criticism of this proposal: Chris Barton, Dave Crocker,
Vincent Schonau
Author's Address
Murray S. Kucherawy
EMail: superuser@gmail.com
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