This is a purely informative rendering of an RFC that includes verified errata. This rendering may not be used as a reference.

The following 'Verified' errata have been incorporated in this document: EID 1580, EID 1581
Network Working Group                                   J. Schoenwaelder
Request for Comments: 5345                      Jacobs University Bremen
Category: Informational                                     October 2008

               Simple Network Management Protocol (SNMP)
            Traffic Measurements and Trace Exchange Formats

Status of This Memo

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


   The IESG thinks that this work is related to IETF work done in the
   Operations and Management Area related to SNMP, but this does not
   prevent publishing.  This RFC is not a candidate for any level of
   Internet Standard.  The IETF disclaims any knowledge of the fitness
   of this RFC for any purpose and notes that the decision to publish is
   not based on IETF review apart from the IETF Last Call on the
   allocation of a URI by IANA and the IESG review for conflict with
   IETF work.  The RFC Editor has chosen to publish this document at its
   discretion.  See RFC 3932 for more information.


   The Simple Network Management Protocol (SNMP) is widely deployed to
   monitor, control, and (sometimes also) configure network elements.
   Even though the SNMP technology is well documented, it remains
   relatively unclear how SNMP is used in practice and what typical SNMP
   usage patterns are.

   This document describes an approach to carrying out large-scale SNMP
   traffic measurements in order to develop a better understanding of
   how SNMP is used in real-world production networks.  It describes the
   motivation, the measurement approach, and the tools and data formats
   needed to carry out such a study.

   This document was produced within the IRTF's Network Management
   Research Group (NMRG), and it represents the consensus of all of the
   active contributors to this group.

Table of Contents

   1. Introduction ....................................................3
   2. Measurement Approach ............................................4
      2.1. Capturing Traffic Traces ...................................5
      2.2. Converting Traffic Traces ..................................6
      2.3. Filtering Traffic Traces ...................................7
      2.4. Storing Traffic Traces .....................................7
      2.5. Analyzing Traffic Traces ...................................8
   3. Analysis of Traffic Traces ......................................9
      3.1. Basic Statistics ...........................................9
      3.2. Periodic versus Aperiodic Traffic ..........................9
      3.3. Message Size and Latency Distributions .....................9
      3.4. Concurrency Levels ........................................10
      3.5. Table Retrieval Approaches ................................10
      3.6. Trap-Directed Polling - Myths or Reality? .................10
      3.7. Popular MIB Definitions ...................................11
      3.8. Usage of Obsolete Objects .................................11
      3.9. Encoding Length Distributions .............................11
      3.10. Counters and Discontinuities .............................11
      3.11. Spin Locks ...............................................12
      3.12. Row Creation .............................................12
   4. Trace Exchange Formats .........................................12
      4.1. XML Representation ........................................12
      4.2. CSV Representation ........................................17
   5. Security Considerations ........................................18
   6. IANA Considerations ............................................19
   7. Acknowledgements ...............................................19
   8. References .....................................................20
      8.1. Normative References ......................................20
      8.2. Informative References ....................................20

1.  Introduction

   The Simple Network Management Protocol (SNMP) was introduced in the
   late 1980s [RFC1052] and has since then evolved to what is known
   today as the SNMP version 3 Framework (SNMPv3) [RFC3410].  While SNMP
   is widely deployed, it is not clear what protocol versions are being
   used, which protocol features are being used, how SNMP usage differs
   in different types of networks or organizations, which information is
   frequently queried, and what typical SNMP interaction patterns occur
   in real-world production networks.

   There have been several publications in the recent past dealing with
   the performance of SNMP in general [SM99][Mal02][Pat01], the impact
   of SNMPv3 security [DSR01][CT04], or the relative performance of SNMP
   compared to Web Services [PDMQ04][PFGL04].  While these papers are
   generally useful to better understand the impact of various design
   decisions and technologies, some of these papers lack a strong
   foundation because authors typically assume certain SNMP interaction
   patterns without having experimental evidence that the assumptions
   are correct.  In fact, there are many speculations on how SNMP is
   being used in real-world production networks, and performance
   comparisons are based on limited test cases, but no systematic
   measurements have been performed and published so far.

   Many authors use the ifTable of the IF-MIB [RFC2863] or the
   tcpConnTable of the TCP-MIB [RFC4022] as a starting point for their
   analysis and comparison.  Despite the fact that there is no evidence
   that operations on these tables dominate SNMP traffic, it is even
   more unclear how these tables are read and which optimizations are
   done (or not done) by real-world applications.  It is also unclear
   what the actual traffic trade-off between periodic polling and more
   aperiodic bulk data retrieval is.  Furthermore, we do not generally
   understand how much traffic is devoted to standardized MIB objects
   and how much traffic deals with proprietary MIB objects and whether
   the operation mix between these object classes differs between
   different operational environments (e.g., backbone networks, access
   networks, enterprise networks).

   This document recommends an approach to collecting, codifying, and
   handling SNMP traffic traces in order to find answers to some of
   these questions.  It describes the tools that have been developed to
   allow network operators to collect traffic traces and to share them
   with research groups interested in analyzing and modeling network
   management interactions.

   While the SNMP trace collection and analysis effort was initiated by
   the research community, network operators can benefit from the SNMP
   measurements too.  Several new tools are being developed as part of

   this effort that can be used to capture and analyze the traffic
   generated by management stations.  This resulting information can
   then be used to improve the efficiency and scalability of management

   The measurement approach described in this document is by design
   limited to the study of SNMP traffic.  Studies of other management
   protocols or the impact of management protocols such as SNMP on other
   traffic sharing the same network resources is left to future efforts.

   This is an Informational document, produced within the IRTF's Network
   Management Research Group (NMRG), and it represents the consensus of
   all of the active contributors to this group.

2.  Measurement Approach

   This section outlines the process of doing SNMP traffic measurements
   and analysis.  The process consists of the following five basic

   1.  Capture raw SNMP traffic traces in pcap packet capture files [1].

   2.  Convert the raw traffic traces into a structured machine and
       human-readable format.  A suitable XML schema has been developed
       for this purpose that captures all SNMP message details.  Another
       more compact comma-separated values (CSV) format has been
       developed that only keeps key information about SNMP messages.

   3.  Filter the converted traffic traces to hide or anonymize
       sensitive information.  While the filtering is conceptually a
       separate step, filtering may actually be implemented as part of
       the previous data conversion step for efficiency reasons.

   4.  Submit the filtered traffic traces to a repository from which
       they can be retrieved and analyzed.  Such a repository may be
       public, under the control of a research group, or under the
       control of a network operator who commits to run analysis scripts
       on the repository on behalf of researchers.

   5.  Analyze the traces by creating and executing analysis scripts
       that extract and aggregate information.

   Several of the steps listed above require the involvement of network
   operators supporting the SNMP measurement projects.  In many cases,
   the filtered XML and CSV representation of the SNMP traces will be
   the interface between the researchers writing analysis scripts and
   the operators involved in the measurement activity.  It is therefore
   important to have a well-defined specification of these interfaces.

   This section provides some advice and concrete hints on how the steps
   listed above can be carried out efficiently.  Some special tools have
   been developed to assist network operators and researchers so that
   the time spent on supporting SNMP traffic measurement projects is
   limited.  The following sections describe the five steps and some
   tools in more detail.

2.1.  Capturing Traffic Traces

   Capturing SNMP traffic traces can be done using packet sniffers such
   as tcpdump [2], wireshark [3], or similar applications.  Some care
   must be taken to specify pcap filter expressions that match the SNMP
   transport endpoints used to carry SNMP traffic (typically 'udp and
   (port 161 or port 162)').  Furthermore, it is necessary to ensure
   that full packets are captured, that is packets are not truncated
   (tcpdump option -s 0).  Finally, it is necessary to carefully select
   the placement of the capturing probe within the network.  Especially
   on bridged LANs, it is important to ensure that all management
   traffic is captured and that the probe has access to all virtual LANs
   carrying management traffic.  This usually requires placing the
   probe(s) close to the management system(s) and configuring dedicated
   monitoring ports on bridged networks.  Some bridges have restrictions
   concerning their monitoring capabilities, and this should be
   investigated and documented where necessary.

   It is recommended to capture at least a full week of data to capture
   diurnal patterns and one cycle of weekly behavior.  Operators are
   strongly encouraged to capture traces over even longer periods of
   time.  Tools such as tcpdump and tcpslice [2] or mergecap and
   editcap [3] can be used to split or merge pcap trace files as needed.

   Several operating systems can offload some of the TCP/IP processing
   such as the calculation of transport layer checksum to network
   interface cards.  Traces that include traffic to/from a capturing
   interface that supports TCP/IP offloading can include incorrect
   transport layer checksums.  The simplest solution is of course to
   turn checksum offloading off while capturing traces (if that is
   feasible without losing too many packets).  The other solution is to
   correct or ignore checksums during the subsequent conversion of the
   raw pcap files.

   It is important to note that the raw pcap files should ideally be
   kept in permanent storage (e.g., compressed and encrypted on a CD ROM
   or DVD).  To verify measurements, it might be necessary to go back to
   the original pcap files if, for example, bugs in the tools described
   below have been detected and fixed.

   For each captured trace, some meta data should be recorded and made
   available.  The meta data should include information such as where
   the trace was collected (name of the network and name of the
   organization owning the network, description of the measurement point
   in the network topology where the trace was collected), when it was
   collected, contact information, the size of the trace, any known
   special events, equipment failures, or major infrastructure changes
   during the data collection period and so on.  It is also extremely
   useful to provide a unique identification.  There are special online
   services such as DatCat [4] where meta data can be stored and which
   provide unique identifiers.

2.2.  Converting Traffic Traces

   Raw traces in pcap format must be converted into a format that is
   human readable while also remaining machine readable for efficient
   post-processing.  Human readability makes it easy for an operator to
   verify that no sensitive data is left in a trace while machine
   readability is needed to efficiently extract relevant information.

   The natural choice here is to use an XML format since XML is human as
   well as machine readable and there are many tools and high-level
   scripting language application programming interfaces (APIs) that can
   be used to process XML documents and to extract meaningful
   information.  However, XML is also pretty verbose, which increases
   processing overhead.  In particular, the usage of XML streaming APIs
   is strongly suggested since APIs that require an in-memory
   representation of XML documents do not handle large traces well.

   Section 4.1 of this document defines a RELAX NG schema [OASISRNG] for
   representing SNMP traffic traces in XML.  The schema captures all
   relevant details of an SNMP message in the XML format.  Note that the
   XML format retains some information about the original ASN.1/BER
   encoding to support message size analysis.

   A lightweight alternative to the full-blown XML representation based
   on comma-separated values (CSV) is defined in Section 4.2.  The CSV
   format only captures selected parts of SNMP messages and is thus more
   compact and faster to process.

   As explained in the previous sections, analysis programs that process
   raw pcap files should have an option to ignore incorrect checksums
   caused by TCP/IP offloading.  In addition, analysis programs that
   process raw pcap files should be able to perform IP reassembly for
   SNMP messages that were fragmented at the IP layer.

   The snmpdump [5] package has been developed to convert raw pcap files
   into XML and CSV format.  The snmpdump program reads pcap, XML, or
   CSV files as input and produces XML files or CSV files as output.

   Specific elements can be filtered as required to protect sensitive

2.3.  Filtering Traffic Traces

   Filtering sensitive data (e.g., access control lists or community
   strings) can be achieved by manipulating the XML representation of an
   SNMP trace.  Standard XSLT processors (e.g., xsltproc [6]) can be
   used for this purpose.  People familiar with the scripting language
   Perl might be interested in choosing a suitable Perl module to
   manipulate XML documents [7].

   The snmpdump program, for example, can filter out sensitive
   information, e.g., by deleting or clearing all XML elements whose
   name matches a regular expression.  Data type specific anonymization
   transformations that maintain lexicographic ordering for values that
   appear in instance identifiers [HS06] can be applied.  Note that
   anonymization transformations are often bound to an initialization
   key and depend on the data being anonymized in an anonymization run.
   As a consequence, users must be careful when they merge data from
   independently anonymized traces.  More information about network
   traffic trace anonymization techniques can be found in [XFA02],
   [FXAM04], [PAPL06], and [RW07].

2.4.  Storing Traffic Traces

   The raw pcap traces as well as the XML / CSV formatted traces should
   be stored in a stable archive or repository.  Such an archive or
   repository might be maintained by research groups (e.g., the NMRG),
   network operators, or both.  It is of key importance that captured
   traces are not lost or modified as they may form the basis of future
   research projects and may also be needed to verify published research
   results.  Access to the archive might be restricted to those who have
   signed some sort of a non-disclosure agreement.

   While this document recommends that raw traces should be kept, it
   must be noted that there are situations where this may not be
   feasible.  The recommendation to keep raw traces may be ignored, for
   example, to comply with data-protection laws or to protect a network
   operator from being forced to provide the data to other

   Lossless compression algorithms embodied in programs such as gzip or
   bzip2 can be used to compress even large trace files down to a size
   where they can be burned on DVDs for cheap long-term storage.

   It must be stressed again that it is important to keep the original
   pcap traces in addition to the XML/CSV formatted traces since the
   pcap traces are the most authentic source of information.
   Improvements in the tool chain may require going back to the original
   pcap traces and rebuilding all intermediate formats from them.

2.5.  Analyzing Traffic Traces

   Scripts that analyze traffic traces must be verified for correctness.
   Ideally, all scripts used to analyze traffic traces will be
   publically accessible so that third parties can verify them.
   Furthermore, sharing scripts will enable other parties to repeat an
   analysis on other traffic traces and to extend such analysis scripts.
   It might be useful to establish a common, versioning repository for
   analysis scripts.

   Due to the availability of XML parsers and the simplicity of the CSV
   format, trace files can be processed with tools written in almost any
   programming language.  However, in order to facilitate a common
   vocabulary and to allow operators to easily read scripts they execute
   on trace files, it is suggested that analysis scripts be written in
   scripting languages such as Perl using suitable Perl modules to
   manipulate XML documents <>.
   Using a scripting language such as Perl instead of system programming
   languages such as C or C++ has the advantage of reducing development
   time and making scripts more accessible to operators who may want to
   verify scripts before running them on trace files that may contain
   sensitive data.

   The snmpdump tool provides an API to process SNMP messages in C/C++.
   While the coding of trace analysis programs in C/C++ should in
   general be avoided for code readability, verifiability, and
   portability reasons, using C/C++ might be the only option in dealing
   with very large traces efficiently.

   Any results produced by analyzing a trace must be interpreted in the
   context of the trace.  The nature of the network, the attachment
   point used to collect the trace, the nature of the applications
   generating SNMP traffic, or the events that happened while the trace
   was collected clearly influence the result.  It is therefore
   important to be careful when drawing general conclusions based on a
   potentially (too) limited data set.

3.  Analysis of Traffic Traces

   This section discusses several questions that can be answered by
   analyzing SNMP traffic traces.  The questions raised in the following
   subsections are meant to be illustrative and no attempt has been made
   to provide a complete list.

3.1.  Basic Statistics

   Basic statistics cover things such as:

   o  protocol version used,

   o  protocol operations used,

   o  message size distribution,

   o  error message type frequency, or

   o  usage of authentication and encryption mechanisms.

   The Object Identifier (OID) names of the objects manipulated can be
   categorized into OID subtrees, for example, to identify
   'standardized', 'proprietary', and 'experimental' objects.

3.2.  Periodic versus Aperiodic Traffic

   SNMP is used to periodically poll devices as well as to retrieve
   information at the request of an operator or application.  The
   periodic polling leads to periodic traffic patterns while on-demand
   information retrieval causes more aperiodic traffic patterns.  It is
   worthwhile to understand what the relationship is between the amount
   of periodic and aperiodic traffic.  It will be interesting to
   understand whether there are multiple levels of periodicity at
   different time scales.

   Periodic polling behavior may be dependent on the application and
   polling engine it uses.  For example, some management platforms allow
   applications to specify how long polled values may be kept in a cache
   before they are polled again.  Such optimizations need to be
   considered when analyzing traces for periodic and aperiodic traffic.

3.3.  Message Size and Latency Distributions

   SNMP messages are size constrained by the transport mappings used and
   the buffers provided by the SNMP engines.  For the further evolution
   of the SNMP framework, it would be useful to know what the actual
   message size distributions are.  It would be useful to understand the

   latency distributions, especially the distribution of the processing
   times by SNMP command responders.  Some SNMP implementations
   approximate networking delays by measuring request-response times,
   and it would be useful to understand to what extent this is a viable

   Some SNMP implementations update their counters from the underlying
   instrumentation following adaptive algorithms, not necessarily
   periodically, and not necessarily on-demand.  The granularity of
   internal counter updates may impact latency measurements and should
   be taken into account.

3.4.  Concurrency Levels

   SNMP allows management stations to retrieve information from multiple
   agents concurrently.  It will be interesting to identify what the
   typical concurrency level is that can be observed on production
   networks or whether management applications prefer more sequential
   ways of retrieving data.

   Furthermore, it will be interesting to analyze how many redundant
   requests coming from applications are processed almost simultaneously
   by a device.  The concurrency level and the amount of redundant
   requests has implications on caching strategies employed by SNMP

3.5.  Table Retrieval Approaches

   Tables can be read in several different ways.  The simplest and most
   inefficient approach is to retrieve tables object-by-object in
   column-by-column order.  More advanced approaches try to read tables
   row-by-row or even multiple-rows-by-multiple-rows.  The retrieval of
   index elements can be suppressed in most cases or only a subset of
   columns of a table are retrieved.  It will be useful to know which of
   these approaches are used on production networks since this has a
   direct implication on agent implementation techniques and caching

3.6.  Trap-Directed Polling - Myths or Reality?

   SNMP is built around a concept called trap-directed polling.
   Management applications are responsible to periodically poll SNMP
   agents to determine their status.  In addition, SNMP agents can send
   traps to notify SNMP managers about events so that SNMP managers can
   adapt their polling strategy and basically react faster than normal
   polling would allow.

   Analysis of SNMP traffic traces can identify whether trap-directed
   polling is actually deployed.  In particular, the question that
   should be addressed is whether SNMP notifications lead to changes in
   the short-term polling behavior of management stations.  In
   particular, it should be investigated to what extent SNMP managers
   use automated procedures to track down the meaning of the event
   conveyed by an SNMP notification.

3.7.  Popular MIB Definitions

   An analysis of object identifier prefixes can identify the most
   popular MIB modules and the most important object types or
   notification types defined by these modules.  Such information would
   be very valuable for the further maintenance and development of these
   and related MIB modules.

3.8.  Usage of Obsolete Objects

   Several objects from the early days have been obsoleted because they
   cannot properly represent today's networks.  A typical example is the
   ipRouteTable that was obsoleted because it was not able to represent
   classless routing, introduced and deployed on the Internet in 1993.
   Some of these obsolete objects are still mentioned in popular
   publications as well as research papers.  It will be interesting to
   find out whether they are also still used by management applications
   or whether management applications have been updated to use the
   replacement objects.

      Depending on the data recorded in a trace, it might be possible to 
   determine the age of devices by looking at the values of objects such
  as sysObjectID and sysDescr [RFC3418].    The age of a device can then
EID 1580 (Verified) is as follows:

Section: 3.8, para #2

Original Text:

   Depending on the data recorded in a trace, it might be possible to
   determine the age of devices by looking at the values of objects such
|  as sysObjectID and sysDecr [RFC3418].  [...]

Corrected Text:

   Depending on the data recorded in a trace, it might be possible to
   determine the age of devices by looking at the values of objects such
|  as sysObjectID and sysDescr [RFC3418].  [...]
See RFC 3418.
be taken into consideration when analyzing the use of obsolete and deprecated objects. 3.9. Encoding Length Distributions It will be useful to understand the encoding length distributions for various data types. Assumptions about encoding length distributions are sometimes used to estimate SNMP message sizes in order to meet transport and buffer size constraints. 3.10. Counters and Discontinuities Counters can experience discontinuities [RFC2578]. A widely used discontinuity indicator is the sysUpTime scalar of the SNMPv2-MIB [RFC3418], which can be reset through a warm start to indicate counter discontinuities. Some MIB modules introduce more specific discontinuity indicators, e.g., the ifCounterDiscontinuityTime of the IF-MIB [RFC2863]. It will be interesting to study to what extent these objects are actually used by management applications to handle discontinuity events. 3.11. Spin Locks Cooperating command generators can use advisory locks to coordinate their usage of SNMP write operations. The snmpSetSerialNo scalar of the SNMPv2-MIB [RFC3418] is the default coarse-grain coordination object. It will be interesting to find out whether there are command generators that coordinate themselves using these spin locks. 3.12. Row Creation Row creation is an operation not natively supported by the protocol operations. Instead, conceptual tables supporting row creation typically provide a control column that uses the RowStatus textual convention defined in the SNMPv2-TC [RFC2579] module. The RowStatus itself supports different row creation modes, namely createAndWait (dribble-mode) and createAndGo (one-shot mode). Different approaches can be used to derive the instance identifier if it does not have special semantics associated with it. It will be useful to study which of the various row creation approaches are actually used by management applications on production networks. 4. Trace Exchange Formats 4.1. XML Representation The XML format has been designed to keep all information associated with SNMP messages. The format is specified in RELAX NG compact notation [OASISRNC]. Freely available tools such as trang [8] can be used to convert RELAX NG compact syntax to other XML schema notations. The XML format can represent SNMPv1, SNMPv2c, and SNMPv3 messages. In case a new version of SNMP is introduced in the future or existing SNMP versions are extended in ways that require changes to the XML format, a new XML format with a different namespace needs to be defined (e.g., by incrementing the version number included in the namespace URI). # Relax NG grammar for the XML SNMP trace format. # # Published as part of RFC 5345. default namespace = "urn:ietf:params:xml:ns:snmp-trace-1.0" start = element snmptrace { packet.elem* } packet.elem = element packet { element time-sec { xsd:unsignedInt }, element time-usec { xsd:unsignedInt }, element src-ip { ipaddress.type }, element src-port { xsd:unsignedInt }, element dst-ip { ipaddress.type }, element dst-port { xsd:unsignedInt }, snmp.elem } snmp.elem = element snmp { length.attrs?, message.elem } message.elem = element version { length.attrs, xsd:int }, element community { length.attrs, xsd:hexBinary }, pdu.elem message.elem |= element version { length.attrs, xsd:int }, element message { length.attrs, element msg-id { length.attrs, xsd:unsignedInt }, element max-size { length.attrs, xsd:unsignedInt }, element flags { length.attrs, xsd:hexBinary }, element security-model { length.attrs, xsd:unsignedInt } }, usm.elem?, element scoped-pdu { length.attrs, element context-engine-id { length.attrs, xsd:hexBinary }, element context-name { length.attrs, xsd:string }, pdu.elem } usm.elem = element usm { length.attrs, element auth-engine-id { length.attrs, xsd:hexBinary }, element auth-engine-boots { length.attrs, xsd:unsignedInt }, element auth-engine-time { length.attrs, xsd:unsignedInt }, element user { length.attrs, xsd:hexBinary }, element auth-params { length.attrs, xsd:hexBinary }, element priv-params { length.attrs, xsd:hexBinary } } pdu.elem = element trap { length.attrs, element enterprise { length.attrs, oid.type }, element agent-addr { length.attrs, ipv4address.type }, element generic-trap { length.attrs, xsd:int }, element specific-trap { length.attrs, xsd:int }, element time-stamp { length.attrs, xsd:int }, element variable-bindings { length.attrs, varbind.elem* } } pdu.elem |= element (get-request | get-next-request | get-bulk-request | set-request | inform-request | snmpV2-trap | response | report) { length.attrs, element request-id { length.attrs, xsd:int }, element error-status { length.attrs, xsd:int }, element error-index { length.attrs, xsd:int }, element variable-bindings { length.attrs, varbind.elem* } } varbind.elem = element varbind { length.attrs, name.elem, value.elem } name.elem = element name { length.attrs, oid.type } value.elem = element null { length.attrs, empty } | element integer32 { length.attrs, xsd:int } | element unsigned32 { length.attrs, xsd:unsignedInt } | element counter32 { length.attrs, xsd:unsignedInt } | element counter64 { length.attrs, xsd:unsignedLong } | element timeticks { length.attrs, xsd:unsignedInt } | element ipaddress { length.attrs, ipv4address.type } | element octet-string { length.attrs, xsd:hexBinary } | element object-identifier { length.attrs, oid.type } | element opaque { length.attrs, xsd:hexBinary } | element no-such-object { length.attrs, empty } | element no-such-instance { length.attrs, empty } | element end-of-mib-view { length.attrs, empty } # The blen attribute indicates the number of octets used by the BER # encoded tag / length / value triple. The vlen attribute indicates # the number of octets used by the BER encoded value alone. length.attrs = ( attribute blen { xsd:unsignedShort }, attribute vlen { xsd:unsignedShort } )? oid.type = xsd:string { pattern = "(([0-1](\.[1-3]?[0-9]))|(2\.(0|([1-9]\d*))))" ~ "(\.(0|([1-9]\d*))){0,126}" }
EID 1581 (Verified) is as follows:

Section: 4.1, pg.15

Original Text:

  oid.type =
    xsd:string {
      pattern =
|       "(([0-1](\.[1-3]?[0-9]))|(2.(0|([1-9]\d*))))" ~

Corrected Text:

  oid.type =
    xsd:string {
      pattern =
|       "(([0-1](\.[1-3]?[0-9]))|(2\.(0|([1-9]\d*))))" ~
Missing backslash disturbs the pattern;
"\." is needed to make the dot literal.
# The types below are for IP addresses. Note that SNMP's buildin # IpAddress type only supports IPv4 addresses; IPv6 addresses are only # introduced to cover SNMP over IPv6 endpoints. ipv4address.type = xsd:string { pattern = "((0|(1[0-9]{0,2})" ~ "|(2(([0-4][0-9]?)|(5[0-5]?)|([6-9]?)))|([3-9][0-9]?))\.){3}" ~ "(0|(1[0-9]{0,2})" ~ "|(2(([0-4][0-9]?)|(5[0-5]?)|([6-9]?)))|([3-9][0-9]?))" } ipv6address.type = xsd:string { pattern = "(([0-9a-fA-F]+:){7}[0-9a-fA-F]+)|" ~ "(([0-9a-fA-F]+:)*[0-9a-fA-F]+)?::(([0-9a-fA-F]+:)*[0-9a-fA-F]+)?" } ipaddress.type = ipv4address.type | ipv6address.type The following example shows an SNMP trace file in XML format containing an SNMPv1 get-next-request message for the OID (sysUpTime) and the response message returned by the agent. <snmptrace xmlns="urn:ietf:params:xml:ns:snmp-trace-1.0"> <packet> <time-sec>1147212206</time-sec> <time-usec>739609</time-usec> <src-ip></src-ip> <src-port>60371</src-port> <dst-ip></dst-ip> <dst-port>12345</dst-port> <snmp blen="42" vlen="40"> <version blen="3" vlen="1">1</version> <community blen="8" vlen="6">7075626c6963</community> <get-next-request blen="29" vlen="27"> <request-id blen="6" vlen="4">1804289383</request-id> <error-status blen="3" vlen="1">0</error-status> <error-index blen="3" vlen="1">0</error-index> <variable-bindings blen="15" vlen="13"> <varbind blen="13" vlen="11"> <name blen="9" vlen="7"></name> <null blen="2" vlen="0"/> </varbind> </variable-bindings> </get-next-request> </snmp> </packet> <packet> <time-sec>1147212206</time-sec> <time-usec>762891</time-usec> <src-ip></src-ip> <src-port>12345</src-port> <dst-ip></dst-ip> <dst-port>60371</dst-port> <snmp blen="47" vlen="45"> <version blen="3" vlen="1">1</version> <community blen="8" vlen="6">7075626c6963</community> <response blen="34" vlen="32"> <request-id blen="6" vlen="4">1804289383</request-id> <error-status blen="3" vlen="1">0</error-status> <error-index blen="3" vlen="1">0</error-index> <variable-bindings blen="20" vlen="18"> <varbind blen="18" vlen="16"> <name blen="10" vlen="8"></name> <unsigned32 blen="6" vlen="4">26842224</unsigned32> </varbind> </variable-bindings> </response> </snmp> </packet> </snmptrace> 4.2. CSV Representation The comma-separated values (CSV) format has been designed to capture only the most relevant information about an SNMP message. In situations where all information about an SNMP message must be captured, the XML format defined above must be used. The CSV format uses the following fields: 1. Timestamp in the format seconds.microseconds since 1970, for example, "1137764769.425484". 2. Source IP address in dotted quad notation (IPv4), for example, "", or compact hexadecimal notation (IPv6), for example, "2001:DB8::1". 3. Source port number represented as a decimal number, for example, "4242". 4. Destination IP address in dotted quad notation (IPv4), for example, "", or compact hexadecimal notation (IPv6), for example, "2001:DB8::1". 5. Destination port number represented as a decimal number, for example, "161". 6. Size of the SNMP message (a decimal number) counted in octets, for example, "123". The size excludes all transport, network, and link-layer headers. 7. SNMP message version represented as a decimal number. The version 0 stands for SNMPv1, 1 for SNMPv2c, and 3 for SNMPv3, for example, "3". 8. SNMP protocol operation indicated by one of the keywords get- request, get-next-request, get-bulk-request, set-request, trap, snmpV2-trap, inform-request, response, report. 9. SNMP request-id in decimal notation, for example, "1511411010". 10. SNMP error-status in decimal notation, for example, "0". 11. SNMP error-index in decimal notation, for example, "0". 12. Number of variable-bindings contained in the varbind-list in decimal notation, for example, "5". 13. For each varbind in the varbind list, three output elements are generated: 1. Object name given as object identifier in dotted decimal notation, for example, "". 2. Object base type name or exception name, that is one of the following: null, integer32, unsigned32, counter32, counter64, timeticks, ipaddress, octet-string, object- identifier, opaque, no-such-object, no-such-instance, and end-of-mib-view. 3. Object value is printed as a number if the underlying base type is numeric. An IPv4 addresses is rendered in the dotted quad notation and an IPv6 address is rendered in the usual hexadecimal notation. An octet string value is printed in hexadecimal format while an object identifier value is printed in dotted decimal notation. In case of an exception, the object value is empty. Note that the format does not preserve the information needed to understand SNMPv1 traps. It is therefore recommended that implementations be able to convert the SNMPv1 trap format into the trap format used by SNMPv2c and SNMPv3, according to the rules defined in [RFC3584]. The activation of trap format conversion should be the user's choice. The following example shows an SNMP trace file in CSV format containing an SNMPv1 get-next-request message for the OID (sysUpTime) and the response message returned by the agent. (Note that the example uses backslash line continuation marks in order to fit the example into the RFC format. Backslash line continuations are not part of the CSV format.) 1147212206.739609,,60371,,12345,42,1,\ get-next-request,1804289383,0,0,1,,null, 1147212206.762891,,12345,,60371,47,1,\ response,1804289383,0,0,1,,timeticks,26842224 5. Security Considerations SNMP traffic traces usually contain sensitive information. It is therefore necessary to (a) remove unwanted information and (b) to anonymize the remaining necessary information before traces are made available for analysis. It is recommended to encrypt traces when they are archived. Implementations that generate CSV or XML traces from raw pcap files should have an option to suppress or anonymize values. Note that instance identifiers of tables also include values, and it might therefore be necessary to suppress or anonymize (parts of) the instance identifiers. Similarly, the packet and message headers typically contain sensitive information about the source and destination of SNMP messages as well as authentication information (community strings or user names). Anonymization techniques can be applied to keep information in traces that could otherwise reveal sensitive information. When using anonymization, values should only be kept when the underlying data type is known and an appropriate anonymization transformation is available (filter-in principle). For values appearing in instance identifiers, it is usually desirable to maintain the lexicographic order. Special anonymization transformations that preserve this property have been developed, although their anonymization strength is usually reduced compared to transformations that do not preserve lexicographic ordering [HS06]. The meta data associated with traces and in particular information about the organization owning a network and the description of the measurement point in the network topology where a trace was collected may be misused to decide/pinpoint where and how to attack a network. Meta data therefore needs to be properly protected. 6. IANA Considerations Per this document, IANA has registered a URI for the SNMP XML trace format namespace in the IETF XML registry [RFC3688]. Following the format in RFC 3688, the following registration has been made: URI: "urn:ietf:params:xml:ns:snmp-trace-1.0" Registrant Contact: The NMRG of the IRTF. XML: N/A, the URI is an XML namespace. 7. Acknowledgements This document was influenced by discussions within the Network Management Research Group (NMRG). Special thanks to Remco van de Meent for writing the initial Perl script that lead to the development of the snmpdump software package and Matus Harvan for his work on lexicographic order preserving anonymization transformations. Aiko Pras contributed ideas to Section 3 while David Harrington helped to improve the readability of this document. Last call reviews have been received from Bert Wijnen, Aiko Pras, Frank Strauss, Remco van de Meent, Giorgio Nunzi, Wes Hardacker, Liam Fallon, Sharon Chisholm, David Perkins, Deep Medhi, Randy Bush, David Harrington, Dan Romascanu, Luca Deri, and Marc Burgess. Karen R. Sollins reviewed the document for the Internet Research Steering Group (IRSG). Jari Arkko, Pasi Eronen, Chris Newman, and Tim Polk provided helpful comments during the Internet Engineering Steering Group (IESG) review. Part of this work was funded by the European Commission under grant FP6-2004-IST-4-EMANICS-026854-NOE. 8. References 8.1. Normative References [RFC2578] McCloghrie, K., Perkins, D., and J. Schoenwaelder, "Structure of Management Information Version 2 (SMIv2)", STD 58, RFC 2578, April 1999. [OASISRNG] Clark, J. and M. Makoto, "RELAX NG Specification", OASIS Committee Specification, December 2001. [OASISRNC] Clark, J., "RELAX NG Compact Syntax", OASIS Committee Specification, November 2002. [RFC3584] Frye, R., Levi, D., Routhier, S., and B. Wijnen, "Coexistence between Version 1, Version 2, and Version 3 of the Internet-standard Network Management Framework", BCP 74, RFC 3584, August 2003. [RFC3688] Mealling, M., "The IETF XML Registry", BCP 81, RFC 3688, January 2004. 8.2. Informative References [RFC1052] Cerf, V., "IAB Recommendations for the development of Internet network management standards", RFC 1052, April 1998. [RFC2579] McCloghrie, K., Perkins, D., and J. Schoenwaelder, "Textual Conventions for SMIv2", STD 58, RFC 2579, April 1999. [RFC3418] Presuhn, R., Ed., "Management Information Base (MIB) for the Simple Network Management Protocol (SNMP)", STD 62, RFC 3418, December 2002. [RFC2863] McCloghrie, K. and F. Kastenholz, "The Interfaces Group MIB", RFC 2863, June 2000. [RFC3410] Case, J., Mundy, R., Partain, D., and B. Stewart, "Introduction and Applicability Statements for Internet- Standard Management Framework", RFC 3410, December 2002. [RFC4022] Raghunarayan, R., "Management Information Base for the Transmission Control Protocol (TCP)", RFC 4022, March 2005. [PDMQ04] Pras, A., Drevers, T., van de Meent, R., and D. Quartel, "Comparing the Performance of SNMP and Web Services based Management", IEEE Transactions on Network and Service Management 1(2), November 2004. [Pat01] Pattinson, C., "A Study of the Behaviour of the Simple Network Management Protocol", Proc. 12th IFIP/IEEE Workshop on Distributed Systems: Operations and Management , October 2001. [DSR01] Du, X., Shayman, M., and M. Rozenblit, "Implementation and Performance Analysis of SNMP on a TLS/TCP Base", Proc. 7th IFIP/IEEE International Symposium on Integrated Network Management , May 2001. [CT04] Corrente, A. and L. Tura, "Security Performance Analysis of SNMPv3 with Respect to SNMPv2c", Proc. 2004 IEEE/IFIP Network Operations and Management Symposium , April 2004. [PFGL04] Pavlou, G., Flegkas, P., Gouveris, S., and A. Liotta, "On Management Technologies and the Potential of Web Services", IEEE Communications Magazine 42(7), July 2004. [SM99] Sprenkels, R. and J. Martin-Flatin, "Bulk Transfers of MIB Data", Simple Times 7(1), March 1999. [Mal02] Malowidzki, M., "GetBulk Worth Fixing", Simple Times 10(1), December 2002. [HS06] Harvan, M. and J. Schoenwaelder, "Prefix- and Lexicographical-order-preserving IP Address Anonymization", IEEE/IFIP Network Operations and Management Symposium NOMS 2006, April 2006. [XFA02] Xu, J., Fan, J., and M. Ammar, "Prefix-Preserving IP Address Anonymization: Measurement-based Security Evaluation and a New Cryptography-based Scheme", 10th IEEE International Conference on Network Protocols ICNP'02, November 2002. [FXAM04] Fan, J., Xu, J., Ammar, M., and S. Moon, "Prefix- Preserving IP Address Anonymization", Computer Networks 46(2), October 2004. [PAPL06] Pang, R., Allman, M., Paxson, V., and J. Lee, "The Devil and Packet Trace Anonymization", Computer Communication Review 36(1), January 2006. [RW07] Ramaswamy, R. and T. Wolf, "High-Speed Prefix-Preserving IP Address Anonymization for Passive Measurement Systems", IEEE Transactions on Networking 15(1), February 2007. URIs [1] <> [2] <> [3] <> [4] <> [5] <> [6] <> [7] <> [8] <> Author's Address Juergen Schoenwaelder Jacobs University Bremen Campus Ring 1 28725 Bremen Germany Phone: +49 421 200-3587 EMail: Full Copyright Statement Copyright (C) The IETF Trust (2008). 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