A Framework for a Network Anomaly Detection Architecture
draft-ietf-nmop-network-anomaly-architecture-06

NMOP                                                             T. Graf
Internet-Draft                                                     W. Du
Intended status: Informational                                  Swisscom
Expires: 25 May 2026                                         P. Francois
                                                           A. Huang-Feng
                                                               INSA-Lyon
                                                        21 November 2025

        A Framework for a Network Anomaly Detection Architecture
            draft-ietf-nmop-network-anomaly-architecture-06

Abstract

   This document describes the motivation and architecture of a Network
   Anomaly Detection Framework and the relationship to other documents
   describing network Symptom semantics and network incident lifecycle.

   The described architecture for detecting IP network service
   interruption is designed to be generic applicable and extensible.
   Different applications are described and examples are referenced with
   open-source running code.

Discussion Venues

   This note is to be removed before publishing as an RFC.

   Discussion of this document takes place on the Operations and
   Management Area Working Group Working Group mailing list
   (nmop@ietf.org), which is archived at
   https://mailarchive.ietf.org/arch/browse/nmop/.

   Source for this draft and an issue tracker can be found at
   https://github.com/ietf-wg-nmop/draft-ietf-nmop-network-anomaly-
   architecture/ .

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/.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Motivation  . . . . . . . . . . . . . . . . . . . . . . .   3
     1.2.  Scope . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   5
     2.1.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   5
     2.2.  Outlier Detection . . . . . . . . . . . . . . . . . . . .   6
     2.3.  Knowledge Based Detection . . . . . . . . . . . . . . . .   7
     2.4.  Machine Learning  . . . . . . . . . . . . . . . . . . . .   8
     2.5.  Data Mesh . . . . . . . . . . . . . . . . . . . . . . . .   8
   3.  Elements of the Architecture  . . . . . . . . . . . . . . . .  10
     3.1.  Service Inventory . . . . . . . . . . . . . . . . . . . .  12
     3.2.  Service Disruption Detection Configuration  . . . . . . .  12
     3.3.  Operational Data Collection . . . . . . . . . . . . . . .  12
     3.4.  Operational Data Aggregation  . . . . . . . . . . . . . .  13
     3.5.  Service Disruption Detection  . . . . . . . . . . . . . .  13
     3.6.  Alarm . . . . . . . . . . . . . . . . . . . . . . . . . .  15
     3.7.  Postmortem  . . . . . . . . . . . . . . . . . . . . . . .  16
     3.8.  Replaying . . . . . . . . . . . . . . . . . . . . . . . .  17
   4.  Implementation Status . . . . . . . . . . . . . . . . . . . .  17
     4.1.  Cosmos Bright Lights  . . . . . . . . . . . . . . . . . .  17
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  18
   6.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  18
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  18
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  18
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  18

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     8.2.  Informative References  . . . . . . . . . . . . . . . . .  20
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  21

1.  Introduction

   Today's highly virtualized large scale IP networks are a challenge
   for network operation to monitor due to its vast number of
   dependencies.  Humans are no longer capable to verify manually all
   the dependencies end to end in a timely manner.

   IP networks are the backbone of today's society.  We individually
   depend on networks fulfilling the purpose of forwarding IP packets
   from a point A to a point B at any time of the day.  A loss of such
   connectivity for a short period of time has today manyfold
   implications that can range from minor to severe.  An interruption
   can lead to being unable to browse the web, watch a soccer game,
   access the company intranet or, even in life threatening situations,
   no longer being able to reach emergency services.  Further, a
   congestion in the network leading to delayed packet forwarding can
   lead to severe repercussions on real-time applications.

   Networks are generally deterministic.  However, the usage of networks
   are only somewhat.  Humans, as in a large group of people, are
   somehow predictable.  There are time of the day patterns in terms of
   when we are eating, sleeping, working or leisure.  And these patterns
   are potentially changing depending on age, profession and cultural
   background.

1.1.  Motivation

   When operational or configurational changes in connectivity services
   are happening, it is crucial for network operators to detect
   interruptions within the network faster than the users utilizing the
   connectivity services.

   In order to achieve this objective, automation in network monitoring
   is required.  The amount of people operating the network are today
   simply outnumbered by the amount of people utilizing connectivity
   services.

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   This automation needs to monitor network changes holistically by
   supervising all 3 network planes simultaneously for a given
   connectivity service on the OSI (Open Systems Interconnection) layer
   3.  The monitoring system needs to detect whether configurational or
   operational State changes, an interface was shutdown by an operator
   versus an interface State went down due to loss of signal on the
   optical layer and wherever it disrupted the service, e.g. the
   received packets from customers are no longer forwarded to the
   desired destination, or not.

   Management plane relates to network node entities.  Where control
   plane in turn propagates a subset o the management plane entities,
   the path reachability, to its neighboring network nodes accross the
   network.  The forwarding plane requires a previously converged
   network topology and received packets to export metrics.

   A State change in control and management plane which are related to
   each other indicate a network topology State change while a State
   change in the forwarding plane describes how the packets are being
   forwarded.  In other words, control and management plane State
   changes can be attributed to network topology State changes whereas
   forwarding plane State changes are related to the outcome of these
   network topology State changes.

   Since changes in networks are happening all the time due to the vast
   number of dependencies, most of the changes are not negatively
   affecting the end to end connectivity due to redundancies in
   networks, a scoring system is needed to indicate how disruptive the
   change is considered.  The scoring system needs to take into account
   the amount of transport sessions, the amount of affected flows and
   whether the detected interruptions are usual or exceptional.

1.2.  Scope

   Such objectives can be achieved by applying checks on network modeled
   time series data that contains semantics describing their
   dependencies across network planes.  These checks can be based on
   domain knowledge or using outlier detection techniques.  Domain-
   knowledge-based techniques applies the expertise of network engineers
   operating a network to understand whether there is an issue impacting
   the customer or not.  On the other hand, outlier detection techniques
   identify measurements that deviate significantly from the norm and
   therefore are considered anomalous.

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   The described scope does not take the connectivity service intent
   into account nor does it verify whether the intent is being achieved
   all the time.  Changes to the service intent causing service
   disruptions are therefore considered service disruptions.  On
   monitoring systems which take the intent into account, this is
   considered as intended.

   Also out of scope of this document are a gradual degredation of a
   connectivity service over a long period of time.  An example would be
   optical fiber degredation which lead to malform packets on IP layer
   and therefore increases packet drops steadily.  Outlier detection
   techniques can be applied here as well but instead of taking the
   network model, the component type and characterstics would be taken
   into context.

2.  Conventions and Definitions

   The key words "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.

2.1.  Terminology

   This document defines the following terms:

   Outlier Detection: Is a systematic approach to identify rare data
   points deviating significantly from the majority.

   Service Disruption Detection (SDD): The process of detecting a
   service degradation by discovering outliers in network monitoring
   data.

   Service Disruption Detection System (SDDS): A system allowing to
   perform SDD.

   Rules: Refers to rules defined by domain experts or artificial
   intelligence in context of detection strategies.  See Section 3.5.1.1
   for details on domain expert rules.

   Additionally it makes use of the terms defined in
   [I-D.ietf-nmop-terminology],
   [I-D.ietf-nmop-network-anomaly-lifecycle] and [RFC8969].

   The following terms are used as defined in
   [I-D.ietf-nmop-terminology] :

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   *  Resource

   *  Event

   *  State

   *  Relevance

   *  Problem

   *  Symptom

   *  Alarm

   Figure 2 in Section 3 of [I-D.ietf-nmop-terminology] shows
   characteristics of observed operational network telemetry metrics.

   Figure 4 in Section 3 of [I-D.ietf-nmop-terminology] shows
   relationships between, state, relevant state, problem, symptom, cause
   and alarm.

   Figure 5 in Section 3 of [I-D.ietf-nmop-terminology] shows
   relationships between problem, symptom and cause.

   The following terms are used as defined in
   [I-D.ietf-nmop-network-anomaly-lifecycle] :

   *  False Positive

   *  False Negative

   *  Confidence Score

   *  Concern Score

   The following terms are used as defined in [RFC8969] :

   *  Service Model

2.2.  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
   document, the following three classes are taken into account:

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   Global outliers:  An outlier is considered "global" if its behavior
      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, in a small time-window, the value gets to 1000, this data
      point is considered a global anomaly.

   Contextual outliers:  An outlier is considered "contextual" if its
      behavior 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.  An
      example of a contextual outlier is when 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
      behavior of each single data point that are part of the anomaly
      are within expected ranges (so they are not anomalous in 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 types of 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 Confidence and a Concern Score between 0 and 1 is
   being calculated.  The higher the Confidence Score value, the higher
   the probability that the observed data point is an outlier.  The
   higher the Concern Score value, the higher the probability that
   observed outlier is impacting the forwarding of the customer packets
   negatively.  Combined together raising the Relevance of the observed
   events.  Anomaly detection: A survey [VAP09] provides and discusses
   an overview on different anomaly detection techniques and the outlier
   detection approach adopted by each.

2.3.  Knowledge Based Detection

   Knowledge-based anomaly detection, a superset of rule-based anomaly
   detection and a subset of semantic-based, Knowledge-based anomaly
   detection: Survey, challenges, and future directions [ASNL25], is a
   technique used to identify anomalies or outliers by comparing them
   against predefined rules or patterns.  This approach relies on the
   use of domain-specific knowledge to set standards, thresholds, or
   rules for what is considered "normal" behavior.  Traditionally, these
   rules are established manually by a knowledgeable network engineer.
   Forward-looking, these rules can be expressed using human and machine

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   readable network protocol derived Symptoms and patterns defined in
   ontologies.

   Additionally, in the context of network anomaly detection, the
   knowledge-based approach works hand in hand with the deterministic
   understanding of the network, which is reflected in network modeling.
   Components are organized into three network planes: the Management
   Plane, the Control Plane, and the Forwarding Plane [RFC9232].  A
   component can relate to a physical, virtual, or configurational
   entity, or to a sum of packets belonging to a flow being forwarded in
   a network.

   Such relationships can be modelled in Service and Infrastructure Maps
   (SIMAP) to automate that process.  [I-D.ietf-nmop-simap-concept]
   defines the concepts for the SIMAP and [I-D.havel-nmop-digital-map]
   defines an application of the SIMAP to network topologies.

   These relationships can also be modeled in Knowledge Graphs Section 5
   of [I-D.mackey-nmop-kg-for-netops] using semantic triples
   [W3C-RDF-concept-triples], where with ontologies, due to its
   declarative form, those semantic triples are machine and human
   readable.  See Section 2.5.2 as an example for an ontology describing
   symptoms.

2.4.  Machine Learning

   Machine learning is commonly used for detecting outliers or
   anomalies.  Typically, unsupervised learning is widely recognized for
   its applicability, given the inherent characteristics of network
   data.  See [VAP09].  Although machine learning requires a sizeable
   amount of high-quality data and considerable advanced training, the
   advantages it offers make these requirements worthwhile.  The power
   of this approach lies in its generalizability, robustness, ability to
   simplify the fine-tuning process, and most importantly, its
   capability to identify anomaly patterns that might go unnoticed to
   the human observer.

2.5.  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.

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2.5.1.  Operational Network 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 Resources.  For example,
      Interface State and statistics modeled 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 exported with IPFIX [RFC7011].

2.5.2.  Analytical Observed Symptoms

   The Service Disruption Detection process takes operational network
   data as input and generates analytical metrics describing Symptoms
   and outlier pattern of the connectivity service disruption.

   The observed Symptoms are categorized into semantic triples
   [W3C-RDF-concept-triples]: action, reason, trigger.  The object is
   the action, describing the change in the network.  The reason is the
   predicate, defining why this change occured and the subject is the
   trigger, which defines what triggered that change.

   Symptom definitions are described in Section 3 of
   [I-D.ietf-nmop-network-anomaly-semantics] and outlier pattern
   semantics in Section 8 of [I-D.ietf-nmop-network-anomaly-lifecycle].
   Both are expressed in YANG Service Models.

   However the semantic could also be expressed with the Semantic Web
   Technology Stack in RDF, RDFS and OWL definitions as described in
   Section 6 of [I-D.mackey-nmop-kg-for-netops].  Together with the
   ontology definitions described in Section 3 of
   [I-D.ietf-nmop-network-anomaly-semantics], a Knowledge Graph can be
   created describing the relationship between the network state and the
   observed Symptom.

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3.  Elements of the Architecture

   The service disruption detection system architecture is aimed at
   detecting service disruptions and is built upon multiple components,
   for which design choices need to be made.  In this section, we
   describe the main components of the architecture, and delve into
   considerations to be made when designing such componenents in an
   implementation.

   The system architecture is illustrated in Figure 1 and its main
   components are described in the following subsections.

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    (1)-------+                     (11)----------------+
    | Service |                     |     Alarm and     |
|-- |Inventory|                     | Problem Management|
|   |         |                     |      System       |
|   +---------+                     +-------------------+
|     |                                      ^     Stream
|     |                                      |
|     |       (12)------+           +-------------------+
|     |       | Post-   | Stream    |   Message Broker  |
|     |       | mortem  | <-------- |  with Analytical  |
|     |       | System  |           |    Network Data   |
|     |       +---------+           +-------------------+
|     |            |                         ^     Stream
|     |            |                         |
|     | (8)        | (3)            +-------------------+ Store
|     | Profile    | Fine           | Alarm Aggregation | Label
|     | and        | Tune           | for Anomaly       | --------|
|     | Generate   | SDD            | Detection         |         |
|     | SDD Config | Config         +-------------------+         |
|     |            |                       ^  ^  ^ Stream         |
|     v            v                       |  |  |       Replay   v
|  (2)-----------------+ (9)        (6)-----------------+    (10)------+
|  | Service Disruption| Schedule   | Service Disruption|    |  Data   |
|  |     Detection     | ---------> |     Detection     |<---| Storage |
|  |   Configuration   | Strategy   |                   |    |         |
|  +-------------------+            +-------------------+    +---------+
|                                      ^ ^ Stream ^ ^ ^           ^
|                                      | |        | | |           |
|                                   (7)-------(5)-------+         |
|                                   | Network |  Data   | Store   |
|---------------------------------> |  Model  |  Aggr.  | --------|
                                    |         | Process | Operational
                                    +---------+---------+ Data
                                           ^  ^  ^ Stream
                                           |  |  |
                                    +-------------------+
                                    |   Message Broker  |
                                    |  with Operational |
                                    |    Network Data   |
                                    +-------------------+
                                           ^  ^  ^ Stream
Subscribe                   Publish        |  |  |
      +-------------------+         (4)-----------------+
      | Network Node with | ------> | Network Telemetry |
----> | Network Telemetry | ------> |  Data Collection  |
      |   Subscription    | ------> |                   |
      +-------------------+         +-------------------+

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      Figure 1: Service Disruption Detection System Architecture

3.1.  Service Inventory

   A service inventory, (1) in Figure 1, is used to obtain a list of the
   connectivity services for which Anomaly Detection is to be performed.
   A service profiling process may be executed on the operational
   network data of the service in order to define a configuration of the
   service disruption detection approach and parameters to be used.

3.2.  Service Disruption Detection Configuration

   Based on this service list and potential preliminary service
   profiling, a configuration of the Service Disruption Detection, (2)
   in Figure 1, is produced.  It defines the set of approaches that need
   to be applied to perform SDD, as well as parameters, grouped in
   templates, that are to be set when executing the algorithms
   performing SDD per se.

   As the service lives on, the configuration may be adapted, (3) in
   Figure 1, as a result of an evolution of the profiling being
   performed.  Postmortem analysis are produced as a result of Events
   impacting the service, or the occurrence of false positives raised by
   the Alarm system.  These postmortem analysis can improve the deployed
   profiles parameters and creation of new customer profiles.  See
   upcoming section Section 3.5.1.3 for details on profiling.

3.3.  Operational Data Collection

   Collection of network monitoring data, (4) in Figure 1, involves the
   management of the subscriptions to network telemetry on nodes of the
   network, and the configuration of the collection infrastructure to
   receive the monitoring data produced by the network.

   The monitoring data produced by the collection infrastructure is then
   streamed through a message broker system, for further processing.

   Networks tend to produce extremely large amounts of monitoring data.
   To preserve scaling and reduce costs, decisions need to be made on
   the duration of retention of such data in storage, and at which level
   of storage they need to be kept.  A retention time need to be set on
   the raw data produced by the collection system, in accordance to
   their utility for further used.  This aspect will be elaborated in
   further sections.

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3.4.  Operational Data Aggregation

   Aggregation, (5) in Figure 1, is the process of producing data sets
   based on collected network monitoring data upon which detection of a
   service disruption can be performed by filtering or aggregating.

   Pre-processing of collected network monitoring data is usually
   performed to reduce input for the Service Disruption Detection
   component since not all metrics are relevant for this use case.  This
   can be achieved in multiple ways, depending on the architecture of
   the SDD component.  As an example, the granularity or cardinality at
   which forwarding plane data is produced by the network may be too
   high for the SDD algorithms, and instead be aggregated into a coarser
   dimension for SDD execution.

   A retention time for the operational data needs to be decided on
   Aggregated data and should reflect the expected further use.  As
   example, the retention time must be set in accordance with the replay
   ability requirement discussed in Section 3.8.

3.5.  Service Disruption Detection

   Service Disruption Detection processes, (6) in Figure 1, decide
   whether a service might be degraded to the point where network
   operation needs to be alerted of an ongoing Problem within the
   network.

   Two key aspects need to be considered when designing the SDD
   component.  First, the way the data is being processed needs to be
   carefully designed, as networks typically produce extremely large
   amounts of data which may hinder the scalability of the architecture.
   Second, the algorithms used to make a decision to alert the operator
   need to be designed in such a way that the operator can trust that a
   targeted Service Disruption will be detected (no false negatives),
   while not spamming the operator with Alarms that do not reflect an
   actual issue within the network (false positives) leading to Alarm
   fatigue.

   Two approaches are typically followed to present the data to the SDD
   system.  Classically, the aggregated data can be stored in a database
   that is polled at regular intervals by the SDD component for decision
   making.  Alternatively, a streaming approach can be followed so as to
   process the data while they are being consumed from the collection
   component.

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   For SDD per-se, two families of algorithms can be decided upon.
   First, knowledge based detection approaches can be used, mimicking
   the process that human operators follow when looking at the data.
   Second, Machine Learning based approaches to detect outliers based
   from prior trained operational network data.

3.5.1.  Knowledge Based

   Knowledge based detection is comprised of several types of knowledge
   sources such as domain knowledge from network engineers
   Section 3.5.1.1 understanding the mechanics of network protocols and
   their implications, knowledge from relationships in the network
   topology Section 3.5.1.2, knowledge derived from Section 3.5.1.3
   where customer, human behavioral related aspects are taken into
   context and finally in Section 3.5.1.4 a combination of that
   knowledge is being applied.

3.5.1.1.  Expert Rules

   Some input to SDD is made of established knowledge from network
   engineers.  This expertise can be used for both Service Disruption
   Detection Configuration or SDD, (2) and (6) in Figure 1 respectively.
   For example, sudden spikes in drop counters from the forwarding plane
   are likely to be attributed to changes in the routing topology.  Or,
   drops in the fowarding plane can manifest in an increase of flow
   counts in the forwarding plane due to the implied congestion and re-
   establishment of application transport sessions.  These network
   behaviours are typically sourced from the experience of operating a
   network infrastructure by human operators, and can be used by an SDD
   engine to trigger alerts.

3.5.1.2.  Network Modeling

   Some input to SDD is made of established knowledge of the network,
   (7) in Figure 1, that is unrelated to the dimensions according to
   which outlier detection is performed.  For example, the knowledge of
   the network infrastructure may be required to perform some service
   disruption detection.  Such data need to be rendered accessible and
   updatable for use by SDD.  They may come from inventories, or
   automated gathering of data from the network itself.

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3.5.1.3.  Data Profiling

   As expert rules cannot be crafted specifically for each customer
   because each customer has a different usage pattern, they need to be
   defined according to pre-established service profiles, (8) in
   Figure 1.  Processing of monitoring data can be performed with
   machine learning methods in order to identify and group patterns into
   clusters and associate clusters with profiles.  External knowledge on
   customer types can also help in associating clusters with profiles.

3.5.1.4.  Detection Strategies

   For a profile, a set of strategies is defined.  Each strategy
   captures one approach to look at the data (as a human operator does)
   to observe if an abnormal situation is arising.  Strategies can use
   both expert rule-based algorithms, as described in Section 3.5.1.1,
   or outlier detection algorithms, as explained in Section 2.2.  Thus,
   a strategy defined as a combination of expert rule-based algorithms
   or outlier detection algorithms that together trigger an alarm when a
   disruption occur.

   When one of the strategies applied for a profile detects a concerning
   global outlier or collective outlier, an Alarm MUST be raised.

   Depending on the implementation of the architecture, a scheduler may
   be needed in order to orchestrate the evaluation of the Alarm levels
   for each strategy applied for a profile, for all service instances
   associated with such profile, as illustrated in (9) from Figure 1.

3.5.2.  Storage

   Storage, (10) in Figure 1, may be required to execute SDD, as some
   algorithms may be relying on historical (aggregated) monitoring data
   in order to detect anomalies.  The cardinality,granularity and
   retention time of historical data should be carefully considered to
   avoid slow and costly retrieval of this information if required for
   SDD analysis.

3.6.  Alarm

   When the SDD component decides that a service is undergoing a
   disruption, an aggregated relevant-state change notification, taking
   the output of multiple Service Disruption Detection processes into
   account, MUST be sent to the Alarm and Problem management system as
   shown in Figure 4 in Section 3 of [I-D.ietf-nmop-terminology] and
   (11) in Figure 1.  Multiple practical aspects need to be taken into
   account in this component.

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   When the issue lasts longer than the interval at which the SDD
   component runs, the relevant-state change mechanism should not create
   multiple notifications to the operator, so as to not overwhelm the
   management of the issue.  However, the information provided along
   with the Alarm should be kept up to date during the full duration of
   the issue.

3.7.  Postmortem

    Network Anomaly
      Detection             Symptoms
 +-------------------+         &
 |   +-----------+   | Network Anomalies
 |   | Detection |---|---------+
 |   |   Stage   |   |         |
 |   +-----------+   |         v
 +---------^---------+    +-------------------+   Labels  +------------+
           |              | Anomaly Detection |---------->| Validation |
           |              |   Label Store     |<----------|   Stage    |
           |              +-------------------+  Revised  +------------+
    +------------+             |                 Labels
    | Refinement |             |
    |   Stage    |<------------+
    +------------+    Historical Symptoms
                               &
                       Network Anomalies

            Figure 2: Anomaly Detection Refinement Lifecycle

   Validation and refinement are performed during Postmortem analysis,
   (12) in Figure 1.

   From an Anomaly Detection Lifecycle point of view, as described in
   [I-D.ietf-nmop-network-anomaly-lifecycle], the Service Disruption
   Detection Configuration evolves over time, iteratively, looping over
   three main phases: detection, validation and refinement.

   The Detection phase produces the Alarms that are sent to the Alarm
   and Problem Management System and at the same time it stores the
   network anomaly and Symptom labels into the Label Store.  This
   enables network engineers to review the labels to validate and edit
   them as needed.

   The Validation stage is typically performed by network engineers
   reviewing the results of the detection and indicating which Symptoms
   and network anomalies have been useful for the identification of

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   Problems in the network.  The original labels from the Service
   Disruption Detection are analyzed and an updated set of more accurate
   labels is provided back to the label store for version-control.

   The resulting labels will be then provided back into the Network
   Anomaly Detection via its refinement capabilities: the refinement is
   about the update of the Service Disruption Detection configuration in
   order to improve the results of the detection (e.g. false positives,
   false negatives, accuracy of the boundaries, etc.).

3.8.  Replaying

   When a service disruption has been detected, it is essential for the
   human operator to be able to analyze the data which led to the
   raising of an Alarm.  It is thus important that a SDDS preserves both
   the data which led to the creation of the Alarm as well as human
   understandable information on why the data led to the raising of an
   Alarm.

   In early stages of operations or when experimenting with a SDDS, it
   is common that the parameters used for SDD are to be fined tuned.
   This process is facilitated by designing the SDDS architecture in a
   way that allows to rerun the SDD algorithms on the same input.

   Data retention, as well as its level, need to be defined in order not
   to sacrifice the ability of replaying SDD execution for the sake of
   improving its accuracy.

4.  Implementation Status

   Note to the RFC-Editor: Please remove this section before publishing.

   This section records the status of known implementations.

4.1.  Cosmos Bright Lights

   This architecture have been developed as part of a proof of concept
   started in September 2022 first in a dedicated network lab
   environment and later in December 2022 in Swisscom production to
   monitor a limited amount of 16 L3 VPN connectivity services.

   At the Applied Networking Research Workshop at IRTF 117 the
   architecture was the first time published in the following academic
   paper: [Ahf23].

   Since December 2022, 20 connectivity service disruptions have been
   monitored and 52 false positives due to time series database
   temporarily not being real-time and missing traffic profiling,

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   comparing to previous week was not applicable, occurred.  Out of 20
   connectivity service disruptions 6 parameters where monitored and 3
   times 1, 8 times 2, 6 times 3, 2 times 4 parameters recognized the
   service disruption.

   A real-time streaming based version has been deployed in Swisscom
   production as a proof of concept in June 2024 monitoring approximate
   >13'000 L3 VPN's concurrently.  Improved profiling capabilities are
   currently under development.

5.  Security Considerations

   Security of the retained data.  Compromised data could reveal
   sensitive information; could prevent valid alarms from being raised;
   or could cause false alarms.

6.  Contributors

   The authors would like to thank Alex Huang Feng, Ahmed Elhassany and
   Vincenzo Riccobene for their valuable contribution.

7.  Acknowledgements

   The authors would like to thank Qin Wu, Ignacio Dominguez Martinez-
   Casanueva, Adrian Farrel, Reshad Rahman, Ruediger Geib, Paul Aitken
   and Yannick Buchs for their review and valuable comments.

8.  References

8.1.  Normative References

   [I-D.havel-nmop-digital-map]
              Havel, O., Claise, B., de Dios, O. G., Elhassany, A., and
              T. Graf, "Modeling the Digital Map based on RFC 8345:
              Sharing Experience and Perspectives", Work in Progress,
              Internet-Draft, draft-havel-nmop-digital-map-02, 21
              October 2024, <https://datatracker.ietf.org/doc/html/
              draft-havel-nmop-digital-map-02>.

   [I-D.ietf-nmop-network-anomaly-lifecycle]
              Riccobene, V., Graf, T., Du, W., and A. H. Feng, "An
              Experiment: Network Anomaly Lifecycle", Work in Progress,
              Internet-Draft, draft-ietf-nmop-network-anomaly-lifecycle-
              03, 8 May 2025, <https://datatracker.ietf.org/doc/html/
              draft-ietf-nmop-network-anomaly-lifecycle-03>.

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   [I-D.ietf-nmop-network-anomaly-semantics]
              Graf, T., Du, W., Feng, A. H., and V. Riccobene, "Semantic
              Metadata Annotation for Network Anomaly Detection", Work
              in Progress, Internet-Draft, draft-ietf-nmop-network-
              anomaly-semantics-03, 8 May 2025,
              <https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
              network-anomaly-semantics-03>.

   [I-D.ietf-nmop-simap-concept]
              Havel, O., Claise, B., de Dios, O. G., and T. Graf,
              "SIMAP: Concept, Requirements, and Use Cases", Work in
              Progress, Internet-Draft, draft-ietf-nmop-simap-concept-
              07, 18 October 2025,
              <https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
              simap-concept-07>.

   [I-D.ietf-nmop-terminology]
              Davis, N., Farrel, A., Graf, T., Wu, Q., and C. Yu, "Some
              Key Terms for Network Fault and Problem Management", Work
              in Progress, Internet-Draft, draft-ietf-nmop-terminology-
              23, 18 August 2025,
              <https://datatracker.ietf.org/doc/html/draft-ietf-nmop-
              terminology-23>.

   [I-D.mackey-nmop-kg-for-netops]
              Mackey, M., Claise, B., Graf, T., Keller, H., Voyer, D.,
              Lucente, P., and I. D. Martinez-Casanueva, "Knowledge
              Graph Framework for Network Operations", Work in Progress,
              Internet-Draft, draft-mackey-nmop-kg-for-netops-03, 2
              September 2025, <https://datatracker.ietf.org/doc/html/
              draft-mackey-nmop-kg-for-netops-03>.

   [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>.

   [RFC8969]  Wu, Q., Ed., Boucadair, M., Ed., Lopez, D., Xie, C., and
              L. Geng, "A Framework for Automating Service and Network
              Management with YANG", RFC 8969, DOI 10.17487/RFC8969,
              January 2021, <https://www.rfc-editor.org/info/rfc8969>.

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   [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

   [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://hal.science/hal-04307611>.

   [ASNL25]   Qadir Khan, A., El Jaouhari, S., Tamani, N., and L.
              Mroueh, "Knowledge-based anomaly detection: Survey,
              challenges, and future directions",
              DOI 10.1016/j.engappai.2024.108996, May 2025,
              <https://hal.science/hal-05055886>.

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

   [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>.

   [VAP09]    Chandola, V., Banerjee, A., and V. Kumar, "Anomaly
              detection: A survey", ACM Computing Surveys 41,
              DOI 10.1145/1541880.1541882, July 2009,
              <https://www.researchgate.net/
              publication/220565847_Anomaly_Detection_A_Survey>.

   [W3C-RDF-concept-triples]
              Cyganiak, R., Wood, D., and M. Lanthaler, "W3C RDF concept
              semantic triples", W3 Consortium, February 2014,
              <https://www.w3.org/TR/rdf-concepts/#section-triples>.

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

   Pierre Francois
   INSA-Lyon
   Lyon
   France
   Email: pierre.francois@insa-lyon.fr

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

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