Internet DRAFT - draft-netana-nmop-network-anomaly-semantics
draft-netana-nmop-network-anomaly-semantics
Network Working Group T. Graf
Internet-Draft W. Du
Intended status: Experimental Swisscom
Expires: 24 July 2024 A. Huang Feng
INSA-Lyon
V. Riccobene
A. Roberto
Huawei
21 January 2024
Semantic Metadata Annotation for Network Anomaly Detection
draft-netana-nmop-network-anomaly-semantics-00
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
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
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 24 July 2024.
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Copyright Notice
Copyright (c) 2024 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
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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 . . . . . . . . . . . . . . . . . . . . . . . . 9
5.2. Overview of the Model for the Incident Semantic
Metadata . . . . . . . . . . . . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 12
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 12
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.1. Normative References . . . . . . . . . . . . . . . . . . 12
8.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
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 paths 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],
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 describe 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
network node has chosen that action.
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.
+==============+=============+====================================+
| Action | Reason | Cause |
+==============+=============+====================================+
| Reachability | Update | Imported |
+--------------+-------------+------------------------------------+
| Reachability | Update | Received |
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+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Received |
+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Peer Down |
+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Suppressed |
+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Stale |
+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Route Policy Filtered |
+--------------+-------------+------------------------------------+
| Reachability | Withdraw | Maximum Number of Prefixes Reached |
+--------------+-------------+------------------------------------+
| Adjacency | Established | Peer |
+--------------+-------------+------------------------------------+
| Adjacency | Established | Link-Layer |
+--------------+-------------+------------------------------------+
| Adjacency | Locally | Peer |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Remotely | Peer |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Locally | Link-Layer |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Remotely | Link-Layer |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Locally | Administrative |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Remotely | Administrative |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Locally | Maximum Number of Prefixes Reached |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Remotely | Maximum Number of Prefixes Reached |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Locally | Transport Connection Failed |
| | Teared Down | |
+--------------+-------------+------------------------------------+
| Adjacency | Remotely | Transport Connection Failed |
| | Teared Down | |
+--------------+-------------+------------------------------------+
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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
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 operational 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).
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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 or counters,
atart 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 annotated as
described in previous section.
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
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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-09,
1 December 2023, <https://datatracker.ietf.org/doc/html/
draft-ietf-ippm-pam-09>.
[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-06, 14 January 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-opsawg-
ipfix-on-path-telemetry-06>.
[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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