Internet-Draft Network Anomaly Semantics March 2024
Graf, et al. Expires 16 September 2024 [Page]
Network Working Group
Intended Status:
T. Graf
W. Du
A. Huang Feng
V. Riccobene
A. Roberto

Semantic Metadata Annotation for Network Anomaly Detection


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

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 16 September 2024.

Table of Contents

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.

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.

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, this applies to operational semantic metadata and service level indicators. 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 and applied in the network anomaly postmortem lifecycle described in [I-D.netana-nmop-network-anomaly-lifecycle] where the applied semantic metadata of this document is refined for each detected anomaly.

4. Observed Symptoms

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


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 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.


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.


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.

Table 1: Describing Symptoms and their Actions, Reason and Cause for Forwarding Plane
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 2 consolidates for the control plane a list of common symptoms with their actions, reasons and causess.

Table 2: Describing Symptoms and their Actions, Reason and Cause for Control Plane
Action Reason Cause
Reachability Update Imported
Reachability Update Received
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 Teared Down Peer
Adjacency Remotely Teared Down Peer
Adjacency Locally Teared Down Link-Layer
Adjacency Remotely Teared Down Link-Layer
Adjacency Locally Teared Down Administrative
Adjacency Remotely Teared Down Administrative
Adjacency Locally Teared Down Maximum Number of Prefixes Reached
Adjacency Remotely Teared Down Maximum Number of Prefixes Reached
Adjacency Locally Teared Down Transport Connection Failed
Adjacency Remotely Teared Down Transport Connection Failed

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

Table 3: Describing Symptoms and their Actions, Reason and Cause for Management Plane
Action Reason Cause
Interface Up Link-Layer
Interface Down Link-Layer
Interface Errors -
Interface Discards -
Interface Unknown Protocol -

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 "anomaly" which represents a collection of symptoms. The anomaly 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 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

6. Security Considerations

The security considerations.

7. Implementation status

This section provides pointers to existing open source implementations of this draft. Note to the RFC-editor: Please remove this before publishing.

7.1. Antagonist

A tool called Antagonist has been implemented during the IETF 119 Hackathon, in order to validate the application of the YANG models defined in this draft. Antagonist provides visual support for two important use cases in the scope of this document:

  • the generation of a ground truth in relation to symptoms and incidents in timeseries data
  • the visual validation of results produced by automated network anomaly detection tools.

The open source code can be found here: [Antagonist]

8. Acknowledgements

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

9. References

9.1. Normative References

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, , <>.
Riccobene, V., Roberto, A., Du, W., Graf, T., and H. Huang Feng, "Antagonist: Anomaly tagging on historical data", <>.
Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, , <>.
Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, , <>.
Bjorklund, M. and L. Berger, Ed., "YANG Tree Diagrams", BCP 215, RFC 8340, DOI 10.17487/RFC8340, , <>.
Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and A. Wang, "Network Telemetry Framework", RFC 9232, DOI 10.17487/RFC9232, , <>.

9.2. Informative References

Dehghani, Z., "Data Mesh", O'Reilly Media, ISBN 9781492092391, , <>.
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-claise-opsawg-collected-data-manifest-06, , <>.
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, , <>.
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, , <>.
Riccobene, V., Roberto, A., Graf, T., Du, W., and A. H. Feng, "Experiment: Network Anomaly Postmortem Lifecycle", Work in Progress, Internet-Draft, draft-netana-nmop-network-anomaly-lifecycle-00, , <>.
Rosen, E. and Y. Rekhter, "BGP/MPLS IP Virtual Private Networks (VPNs)", RFC 4364, DOI 10.17487/RFC4364, , <>.
Quittek, J., Bryant, S., Claise, B., Aitken, P., and J. Meyer, "Information Model for IP Flow Information Export", RFC 5102, DOI 10.17487/RFC5102, , <>.
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, , <>.
Yourtchenko, A., Aitken, P., and B. Claise, "Cisco-Specific Information Elements Reused in IP Flow Information Export (IPFIX)", RFC 7270, DOI 10.17487/RFC7270, , <>.
Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP Monitoring Protocol (BMP)", RFC 7854, DOI 10.17487/RFC7854, , <>.
Bjorklund, M., "A YANG Data Model for Interface Management", RFC 8343, DOI 10.17487/RFC8343, , <>.
Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T. Arumugam, "A YANG Data Model for Service Assurance", RFC 9418, DOI 10.17487/RFC9418, , <>.
Chandola, V., Banerjee, A., and V. Kumar, "Anomaly detection: A survey", IETF 117, Applied Networking Research Workshop, DOI 10.1145/1541880.1541882, , <>.

Authors' Addresses

Thomas Graf
Binzring 17
CH-8045 Zurich
Wanting Du
Binzring 17
CH-8045 Zurich
Alex Huang Feng
Vincenzo Riccobene
Antonio Roberto