draft-netana-opsawg-nmrg-network-anomaly-semantics-01.txt

Network Working Group                                            T. Graf
Internet-Draft                                                     W. Du
Intended status: Experimental                                   Swisscom
Expires: 7 May 2024                                        A. Huang Feng
                                                               INSA-Lyon
                                                            V. Riccobene
                                                              A. Roberto
                                                                  Huawei
                                                         4 November 2023


       Semantic Metadata Annotation for Network Anomaly Detection
         draft-netana-opsawg-nmrg-network-anomaly-semantics-01

Abstract

   This document explains why and how semantic metadata annotation helps
   to test, validate and compare outlier detection, supports supervised
   and semi-supervised machine learning development, enables data
   exchange among network operators, vendors and academia and make
   anomalies for humans apprehensible.  The proposed semantics uniforms
   the network anomaly data exchange between and among operators and
   vendors to improve their network outlier detection systems.

Requirements Language

   The keywords "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 7 May 2024.



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Copyright Notice

   Copyright (c) 2023 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 (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components
   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Outlier Detection . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Data Mesh . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Observed Symptoms . . . . . . . . . . . . . . . . . . . . . .   4
   5.  Semantic Metadata . . . . . . . . . . . . . . . . . . . . . .   8
     5.1.  Overview of the Model for the Symptom Semantic
           Metadata  . . . . . . . . . . . . . . . . . . . . . . . .   8
     5.2.  Overview of the Model for the Incident Semantic
           Metadata  . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  11
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  11
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  11
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction

   Network Anomaly Detection Architecture [Ahf23] provides an overall
   introduction into how anomaly detection is being applied into the IP
   network domain and which operational data is needed.  It approaches
   the problem space by automating what a Network Engineer would
   normally do when verifying a network connectivity service.  Monitor
   from different network plane perspectives to understand wherever one
   network plane affects another negatively.

   In order to fine tune outlier detection, the results provided as
   analytical data need to be reviewed by a Network Engineer.  Keeping
   the human out of the monitoring but still involving him in the alert
   verification loop.





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   This document describes what information is needed to understand the
   output of the outlier detection for a Network Engineer, but also at
   the same time is semantically structured that it can be used for
   outlier detection testing by comparing the results systematically and
   set a baseline for supervised machine learning which requires labeled
   operational data.

2.  Outlier Detection

   Outlier Detection, also known as anomaly detection, describes a
   systematic approach to identify rare data points deviating
   significantly from the majority.  Outliers can manifest as single
   data point or as a sequence of data points.  There are multiple ways
   in general to classify anomalies, but for the context of this draft,
   the following three classes are taken into account:

   Global outliers:  An outlier is considered "global" if its behaviour
      is outside the entirety of the considered data set.  For example,
      if the average dropped packet count is between 0 and 10 per minute
      and a small time-window gets the value 1000, this is considered a
      global anomaly.

   Contextual outliers:  An outlier is considered "contextual" if its
      behaviour is within a normal (expected) range, but it would not be
      expected based on some context.  Context can be defined as a
      function of multiple parameters, such as time, location, etc.  For
      example, the forwarded packet volume overnight reaches levels
      which might be totally normal for the daytime, but anomalous and
      unexpected for the nighttime.

   Collective outliers:  An outlier is considered "collective" if the
      behaviour of each single data point that are part of the anomaly
      are within expected ranges (so they are not anomalous it either a
      contextual or a global sense), but the group, taking all the data
      points together, is.  Note that the group can be made within a
      single time series (a sequence of data points is anomalous) or
      across multiple metrics (e.g. if looking at two metrics together,
      the combined behavior turns out to be anomalous).  In Network
      Telemetry time series, one way this can manifest is that the
      amount of network path and interface state changes matches the
      time range when the forwarded packet volume decreases as a group.

   For each outlier a score between 0 and 1 is being calculated.  The
   higher the value, the higher the probability that the observed data
   point is an outlier.  Anomaly detection: A survey [VAP09] gives
   additional details on anomaly detection and its types.





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3.  Data Mesh

   The Data Mesh [Deh22] Architecture distinguishes between operational
   and analytical data.  Operational data refers to collected data from
   operational systems.  While analytical data refers to insights gained
   from operational data.

   In terms of network observability, semantics of operational network
   metrics are defined by IETF and are categorized as described in the
   Network Telemetry Framework [RFC9232] in the following three
   different network planes:

   Management Plane:  Time series data describing the state changes and
      statistics of a network node and its components.  For example,
      Interface state and statistics modelled in ietf-interfaces.yang
      [RFC8343]

   Control Plane:  Time series data describing the state and state
      changes of network reachability.  For example, BGP VPNv6 unicast
      updates and withdrawals exported in BGP Monitoring Protocol (BMP)
      [RFC7854] and modeled in BGP [RFC4364]

   Forwarding Plane:  Time series data describing the forwarding
      behavior of packets and its data-plane context.  For example,
      dropped packet count modelled in IPFIX entity
      forwardingStatus(IE89) [RFC7270] and packetDeltaCount(IE2)
      [RFC5102] and exportet with IPFIX [RFC7011].

   In terms of network observability, semantics of analytical data
   refers to incident notifications or service level indicators.  For
   example the incident notification described in Section 7.2 of
   [I-D.feng-opsawg-incident-management], the health status and symptoms
   described in the Service Assurance Intend Based Networking [RFC9418]
   or the precision availability metrics defined in [I-D.ietf-ippm-pam]
   or network anomalies and its symptoms as described in this document.

4.  Observed Symptoms

   In this section observed network symptoms are specified and
   categorized according to the following scheme:

   Action:  Which action the network node performed for a packet in the
      Forwarding Plane, a path or adjacency in the Control Plane or
      state or statistical changes in the Management Plane.  For
      Forwarding Plane we distinguish between missing, where the drop
      occured outside the measured network node, drop and on-path delay,
      which was measured on the network node.  For Control Plane we
      distinguish between reachability, which refers to a change in the



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      routing or forwarding information base (RIB/FIB) and adjcacency
      which refers to a change in peering or link-layer resolution.  For
      Management Plane we refer to state or statistical changes on
      interfaces.

   Reason:  For each action one or more reasons describing why this
      action was used.  For Drops in Forwarding Plane we distinguish
      between Unreachable because network layer reachability information
      was missing, administered because an administrator configured a
      rule preventing the forwarding for this packet and Corrupt where
      the network node was unable to determine where to forward to due
      to packet, software or hardware error.  For On-Path Delay we
      distinguish between Minimum, Average and Maximum Delay for a given
      Flow.  For Control Plane wherever a the reachability was updated
      or withdrawn or the adjcacency was established or teared down.
      For Management Plane we distinguish between interfaces states up
      and down, and statistical erros, discards or unknown protocol
      counters.

   Cause:  For each reason one or more cause describe the cause why the
      action was chosen..

   Table 1 consolidates for the forwarding plane a list of common
   symptoms with their Actions, Reasons and Causes.



























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            +=========+==============+========================+
            | Action  | Reason       | Cause                  |
            +=========+==============+========================+
            | Missing | Previous     | Time                   |
            +---------+--------------+------------------------+
            | Drop    | Unreachable  | next-hop               |
            +---------+--------------+------------------------+
            | Drop    | Unreachable  | link-layer             |
            +---------+--------------+------------------------+
            | Drop    | Unreachable  | Time To Life expired   |
            +---------+--------------+------------------------+
            | Drop    | Unreachable  | Fragmentation needed   |
            |         |              | and Don't Fragment set |
            +---------+--------------+------------------------+
            | Drop    | Administered | Access-List            |
            +---------+--------------+------------------------+
            | Drop    | Administered | Unicast Reverse Path   |
            |         |              | Forwarding             |
            +---------+--------------+------------------------+
            | Drop    | Administered | Discard Route          |
            +---------+--------------+------------------------+
            | Drop    | Administered | Policed                |
            +---------+--------------+------------------------+
            | Drop    | Administered | Shaped                 |
            +---------+--------------+------------------------+
            | Drop    | Corrupt      | Bad Packet             |
            +---------+--------------+------------------------+
            | Drop    | Corrupt      | Bad Egress Interface   |
            +---------+--------------+------------------------+
            | Delay   | Min          | -                      |
            +---------+--------------+------------------------+
            | Delay   | Mean         | -                      |
            +---------+--------------+------------------------+
            | Delay   | Max          | -                      |
            +---------+--------------+------------------------+

              Table 1: Describing Symptoms and their Actions,
                   Reason and Cause for Forwarding Plane

   Table 2 consolidates for the control plane a list of common symptoms
   with their actions, reasons and causess.










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                +==============+=============+============+
                | Action       | Reason      | Cause      |
                +==============+=============+============+
                | Reachability | Update      | Imported   |
                +--------------+-------------+------------+
                | Reachability | Update      | Received   |
                +--------------+-------------+------------+
                | Reachability | Withdraw    | Received   |
                +--------------+-------------+------------+
                | Reachability | Withdraw    | Peer Down  |
                +--------------+-------------+------------+
                | Adjacency    | Established | Peer       |
                +--------------+-------------+------------+
                | Adjacency    | Established | Link-Layer |
                +--------------+-------------+------------+
                | Adjacency    | Teared Down | Peer       |
                +--------------+-------------+------------+
                | Adjacency    | Teared Down | Link-Layer |
                +--------------+-------------+------------+

                   Table 2: Describing Symptoms and their
                   Actions, Reason and Cause for Control
                                   Plane

   Table 3 consolidates for the management plane a list of common
   symptoms with their Actions, Reasons and Causes.

               +===========+==================+============+
               | Action    | Reason           | Cause      |
               +===========+==================+============+
               | Interface | Up               | Link-Layer |
               +-----------+------------------+------------+
               | Interface | Down             | Link-Layer |
               +-----------+------------------+------------+
               | Interface | Errors           | -          |
               +-----------+------------------+------------+
               | Interface | Discards         | -          |
               +-----------+------------------+------------+
               | Interface | Unknown Protocol | -          |
               +-----------+------------------+------------+

                   Table 3: Describing Symptoms and their
                  Actions, Reason and Cause for Management
                                   Plane







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5.  Semantic Metadata

   Metadata adds additional context to data.  For instance, in networks
   the software version of a network node where management plane metrics
   are obtained from as described
   in[I-D.claise-opsawg-collected-data-manifest].  Where in Semantic
   Metadata the meaning or ontology of the annotated data is being
   described.  In this section, a YANG model is defined, in order to
   provide a structure for the metadata related to anomalies happening
   in the network.  The module is intended to describe the metadata used
   to "annotate" the observability data collected from the network
   nodes, which can include time series data and logs, as well as other
   forms of data that is "time-bounded".  The aspects discussed so far
   in this document are grouped under the concept of "incident" which
   represents a collection of symptoms.  The incident overall has a set
   of parameters that describe the overall behavior of the network in a
   given time-window including all the spotted symptoms (network
   anomalies).

5.1.  Overview of the Model for the Symptom Semantic Metadata

   Figure 1 contains the YANG tree diagram [RFC8340] of the ietf-
   symptom-semantic-metadata module.  For each symptom, the following
   parameters have been assigned: - a unique ID for identification; a
   description of the symptom; - a list of affected metrics / counters;
   - Start and End time, to specify the time-window; - a confident
   score, indicating how accurate the symptom was detected; - a concern
   score, indicating how critical the symptom is; - the source,
   indicating if it has been identified by a network expert or an
   algorithm; - the tags with key value where; - Action, Reason and
   Cause, can be dannotaed as described in previous section.




















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                 module: ietf-symptom-semantic-metadata
                   +--rw symptom
                     +--rw id                         yang:uuid
                     +--rw event-id                   yang:uuid
                     +--rw description                string
                     +--rw start-time                 yang:date-and-time
                     +--rw end-time                   yang:date-and-time
                     +--rw confidence-score           float
                     +--rw concern-score?             float
                     +--rw tags* [key]
                     |  +--rw key      string
                     |  +--rw value    string
                     +--rw (pattern)?
                     |  +--:(drop)
                     |  |  +--rw drop                 empty
                     |  +--:(spike)
                     |  |  +--rw spike                empty
                     |  +--:(mean-shift)
                     |  |  +--rw mean-shift           empty
                     |  +--:(seasonality-shift)
                     |  |  +--rw seasonality-shift    empty
                     |  +--:(trend)
                     |  |  +--rw trend                empty
                     |  +--:(other)
                     |     +--rw other                string
                     +--rw source
                         +--rw (source-type)
                         |  +--:(human)
                         |  |  +--rw human        empty
                         |  +--:(algorithm)
                         |     +--rw algorithm    empty
                         +--rw name?              string


       Figure 1: YANG tree diagram for ietf-symptom-semantic-metadata

5.2.  Overview of the Model for the Incident Semantic Metadata

   Figure 2 contains the YANG tree diagram [RFC8340] of the ietf-
   incident-semantic-metadata module.  The semantic of the fields of
   this model is in line with what specified in the previous section,
   for the symptom-semantic-metadata-tree.









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                module: ietf-incident-semantic-metadata
                  +--rw incident
                    +--rw id             yang:uuid
                    +--rw description    string
                    +--rw start-time     yang:date-and-time
                    +--rw end-time       yang:date-and-time
                    +--rw symptoms* []
                    |  +--rw symptom
                    |     +--rw id                         yang:uuid
                    |     +--rw event-id                   yang:uuid
                    |     +--rw description                string
                    |     +--rw start-time                 yang:date-and-time
                    |     +--rw end-time                   yang:date-and-time
                    |     +--rw confidence-score           float
                    |     +--rw concern-score?             float
                    |     +--rw tags* [key]
                    |     |  +--rw key      string
                    |     |  +--rw value    string
                    |     +--rw (pattern)?
                    |     |  +--:(drop)
                    |     |  |  +--rw drop                 empty
                    |     |  +--:(spike)
                    |     |  |  +--rw spike                empty
                    |     |  +--:(mean-shift)
                    |     |  |  +--rw mean-shift           empty
                    |     |  +--:(seasonality-shift)
                    |     |  |  +--rw seasonality-shift    empty
                    |     |  +--:(trend)
                    |     |  |  +--rw trend                empty
                    |     |  +--:(other)
                    |     |     +--rw other                string
                    |     +--rw source
                    |        +--rw (source-type)
                    |        |  +--:(human)
                    |        |  |  +--rw human        empty
                    |        |  +--:(algorithm)
                    |        |     +--rw algorithm    empty
                    |        +--rw name?              string
                    +--rw source
                        +--rw (type)
                        |  +--:(human)
                        |  |  +--rw human        empty
                        |  +--:(algorithm)
                        |     +--rw algorithm    empty
                        +--rw name?              string


   Figure 2: YANG tree diagram for ietf-incident-semantic-metadata



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6.  Security Considerations

   The security considerations.

7.  Acknowledgements

   The authors would like to thank xxx for their review and valuable
   comments.

8.  References

8.1.  Normative References

   [Ahf23]    Huang Feng, A., "Daisy: Practical Anomaly Detection in
              large BGP/MPLS and BGP/SRv6 VPN Networks", IETF 117,
              Applied Networking Research Workshop,
              DOI 10.1145/3606464.3606470, July 2023,
              <https://anrw23.hotcrp.com/doc/anrw23-paper8.pdf>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

   [RFC8340]  Bjorklund, M. and L. Berger, Ed., "YANG Tree Diagrams",
              BCP 215, RFC 8340, DOI 10.17487/RFC8340, March 2018,
              <https://www.rfc-editor.org/info/rfc8340>.

   [RFC9232]  Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
              A. Wang, "Network Telemetry Framework", RFC 9232,
              DOI 10.17487/RFC9232, May 2022,
              <https://www.rfc-editor.org/info/rfc9232>.

8.2.  Informative References

   [Deh22]    Dehghani, Z., "Data Mesh", O'Reilly Media,
              ISBN 9781492092391, March 2022,
              <https://www.oreilly.com/library/view/data-
              mesh/9781492092384/>.

   [I-D.claise-opsawg-collected-data-manifest]
              Claise, B., Quilbeuf, J., Lopez, D., Martinez-Casanueva,
              I. D., and T. Graf, "A Data Manifest for Contextualized
              Telemetry Data", Work in Progress, Internet-Draft, draft-



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              claise-opsawg-collected-data-manifest-06, 10 March 2023,
              <https://datatracker.ietf.org/doc/html/draft-claise-
              opsawg-collected-data-manifest-06>.

   [I-D.feng-opsawg-incident-management]
              Feng, C., Hu, T., Contreras, L. M., Graf, T., Wu, Q., Yu,
              C., and N. Davis, "Incident Management for Network
              Services", Work in Progress, Internet-Draft, draft-feng-
              opsawg-incident-management-03, 23 October 2023,
              <https://datatracker.ietf.org/doc/html/draft-feng-opsawg-
              incident-management-03>.

   [I-D.ietf-ippm-pam]
              Mirsky, G., Halpern, J. M., Min, X., Clemm, A., Strassner,
              J., and J. François, "Precision Availability Metrics for
              Services Governed by Service Level Objectives (SLOs)",
              Work in Progress, Internet-Draft, draft-ietf-ippm-pam-08,
              18 October 2023, <https://datatracker.ietf.org/doc/html/
              draft-ietf-ippm-pam-08>.

   [I-D.ietf-opsawg-ipfix-on-path-telemetry]
              Graf, T., Claise, B., and A. H. Feng, "Export of On-Path
              Delay in IPFIX", Work in Progress, Internet-Draft, draft-
              ietf-opsawg-ipfix-on-path-telemetry-04, 6 July 2023,
              <https://datatracker.ietf.org/doc/html/draft-ietf-opsawg-
              ipfix-on-path-telemetry-04>.

   [RFC4364]  Rosen, E. and Y. Rekhter, "BGP/MPLS IP Virtual Private
              Networks (VPNs)", RFC 4364, DOI 10.17487/RFC4364, February
              2006, <https://www.rfc-editor.org/info/rfc4364>.

   [RFC5102]  Quittek, J., Bryant, S., Claise, B., Aitken, P., and J.
              Meyer, "Information Model for IP Flow Information Export",
              RFC 5102, DOI 10.17487/RFC5102, January 2008,
              <https://www.rfc-editor.org/info/rfc5102>.

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,
              <https://www.rfc-editor.org/info/rfc7011>.

   [RFC7270]  Yourtchenko, A., Aitken, P., and B. Claise, "Cisco-
              Specific Information Elements Reused in IP Flow
              Information Export (IPFIX)", RFC 7270,
              DOI 10.17487/RFC7270, June 2014,
              <https://www.rfc-editor.org/info/rfc7270>.




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   [RFC7854]  Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP
              Monitoring Protocol (BMP)", RFC 7854,
              DOI 10.17487/RFC7854, June 2016,
              <https://www.rfc-editor.org/info/rfc7854>.

   [RFC8343]  Bjorklund, M., "A YANG Data Model for Interface
              Management", RFC 8343, DOI 10.17487/RFC8343, March 2018,
              <https://www.rfc-editor.org/info/rfc8343>.

   [RFC9418]  Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
              Arumugam, "A YANG Data Model for Service Assurance",
              RFC 9418, DOI 10.17487/RFC9418, July 2023,
              <https://www.rfc-editor.org/info/rfc9418>.

   [VAP09]    Chandola, V., Banerjee, A., and V. Kumar, "Anomaly
              detection: A survey", IETF 117, Applied Networking
              Research Workshop, DOI 10.1145/1541880.1541882, July 2009,
              <https://www.researchgate.net/
              publication/220565847_Anomaly_Detection_A_Survey>.

Authors' Addresses

   Thomas Graf
   Swisscom
   Binzring 17
   CH-8045 Zurich
   Switzerland
   Email: thomas.graf@swisscom.com


   Wanting Du
   Swisscom
   Binzring 17
   CH-8045 Zurich
   Switzerland
   Email: wanting.du@swisscom.com


   Alex Huang Feng
   INSA-Lyon
   Lyon
   France
   Email: alex.huang-feng@insa-lyon.fr








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   Vincenzo Riccobene
   Huawei
   Dublin
   Ireland
   Email: vincenzo.riccobene@huawei-partners.com


   Antonio Roberto
   Huawei
   Dublin
   Ireland
   Email: antonio.roberto@huawei.com







































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