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draft-netana-opsawg-nmrg-network-anomaly-semantics-02.xml
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<?xml version="1.0" encoding="US-ASCII"?>
<!DOCTYPE rfc SYSTEM "rfc2629.dtd">
<?rfc toc="yes"?>
<?rfc tocompact="yes"?>
<?rfc tocdepth="2"?>
<?rfc tocindent="yes"?>
<?rfc symrefs="yes"?>
<?rfc sortrefs="yes"?>
<?rfc comments="yes"?>
<?rfc inline="yes"?>
<?rfc compact="yes"?>
<?rfc subcompact="no"?>
<rfc category="exp"
docName="draft-netana-opsawg-nmrg-network-anomaly-semantics-01"
ipr="trust200902">
<front>
<title abbrev="Network Anomaly Semantics">Semantic Metadata Annotation for
Network Anomaly Detection</title>
<author fullname="Thomas Graf" initials="T" surname="Graf">
<organization>Swisscom</organization>
<address>
<postal>
<street>Binzring 17</street>
<city>Zurich</city>
<code>8045</code>
<country>Switzerland</country>
</postal>
<email>[email protected]</email>
</address>
</author>
<author fullname="Wanting Du" initials="W" surname="Du">
<organization>Swisscom</organization>
<address>
<postal>
<street>Binzring 17</street>
<city>Zurich</city>
<code>8045</code>
<country>Switzerland</country>
</postal>
<email>[email protected]</email>
</address>
</author>
<author fullname="Alex Huang Feng" initials="A." surname="Huang Feng">
<organization>INSA-Lyon</organization>
<address>
<postal>
<street/>
<city>Lyon</city>
<region/>
<code/>
<country>France</country>
</postal>
<phone/>
<facsimile/>
<email>[email protected]</email>
<uri/>
</address>
</author>
<author fullname="Vincenzo Riccobene" initials="V." surname="Riccobene">
<organization>Huawei</organization>
<address>
<postal>
<street/>
<city>Dublin</city>
<region/>
<code/>
<country>Ireland</country>
</postal>
<phone/>
<facsimile/>
<email>[email protected]</email>
<uri/>
</address>
</author>
<author fullname="Antonio Roberto" initials="A." surname="Roberto">
<organization>Huawei</organization>
<address>
<postal>
<street/>
<city>Dublin</city>
<region/>
<code/>
<country>Ireland</country>
</postal>
<phone/>
<facsimile/>
<email>[email protected]</email>
<uri/>
</address>
</author>
<date day="04" month="November" year="2023"/>
<abstract>
<t>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.</t>
</abstract>
<note title="Requirements Language">
<t>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
<xref target="RFC2119"/> <xref target="RFC8174"/> when, and only when,
they appear in all capitals, as shown here.</t>
</note>
</front>
<middle>
<section anchor="Introduction" title="Introduction">
<t><xref target="Ahf23">Network Anomaly Detection Architecture</xref>
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.</t>
<t>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.</t>
<t>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.</t>
</section>
<section anchor="Outlier_Detection" title="Outlier Detection">
<t>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:</t>
<dl>
<dt>Global outliers:</dt>
<dd>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.</dd>
</dl>
<dl>
<dt>Contextual outliers:</dt>
<dd>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.</dd>
</dl>
<dl>
<dt>Collective outliers:</dt>
<dd>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.</dd>
</dl>
<t>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. <xref target="VAP09">Anomaly detection: A
survey</xref> gives additional details on anomaly detection and its
types.</t>
</section>
<section anchor="Data_Mesh" title="Data Mesh">
<t>The <xref target="Deh22">Data Mesh</xref> 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.</t>
<t>In terms of network observability, semantics of operational network
metrics are defined by IETF and are categorized as described in the
Network Telemetry Framework <xref target="RFC9232"/> in the following
three different network planes:</t>
<dl>
<dt>Management Plane:</dt>
<dd>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 <xref
target="RFC8343"/></dd>
</dl>
<dl>
<dt>Control Plane:</dt>
<dd>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) <xref target="RFC7854"/> and
modeled in BGP <xref target="RFC4364"/></dd>
</dl>
<dl>
<dt>Forwarding Plane:</dt>
<dd>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) <xref target="RFC7270"/> and
packetDeltaCount(IE2) <xref target="RFC5102"/> and exportet with IPFIX
<xref target="RFC7011"/>.</dd>
</dl>
<t>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 <xref
target="I-D.feng-opsawg-incident-management"/>, the health status and
symptoms described in the Service Assurance Intend Based Networking
<xref target="RFC9418"/> or the precision availability metrics defined
in <xref target="I-D.ietf-ippm-pam"/> or network anomalies and its
symptoms as described in this document.</t>
</section>
<section anchor="Observed_Symptoms" title="Observed Symptoms">
<t>In this section observed network symptoms are specified and
categorized according to the following scheme:</t>
<dl>
<dt>Action:</dt>
<dd>
<t>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.</t>
</dd>
</dl>
<dl>
<dt>Reason:</dt>
<dd>
<t>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.</t>
</dd>
</dl>
<dl>
<dt>Cause:</dt>
<dd>
<t>For each reason one or more cause describe the cause why the
action was chosen..</t>
</dd>
</dl>
<t><xref target="symptom_forwarding_plane_actions_table"/> consolidates
for the forwarding plane a list of common symptoms with their Actions,
Reasons and Causes.</t>
<table align="center" anchor="symptom_forwarding_plane_actions_table">
<name slugifiedName="symptom_forwarding_plane_actions">Describing
Symptoms and their Actions, Reason and Cause for Forwarding
Plane</name>
<thead>
<tr>
<th align="left" colspan="1" rowspan="1">Action</th>
<th align="left" colspan="1" rowspan="1">Reason</th>
<th align="left" colspan="1" rowspan="1">Cause</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" colspan="1" rowspan="1">Missing</td>
<td align="left" colspan="1" rowspan="1">Previous</td>
<td align="left" colspan="1" rowspan="1">Time</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Unreachable</td>
<td align="left" colspan="1" rowspan="1">next-hop</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Unreachable</td>
<td align="left" colspan="1" rowspan="1">link-layer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Unreachable</td>
<td align="left" colspan="1" rowspan="1">Time To Life expired</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Unreachable</td>
<td align="left" colspan="1" rowspan="1">Fragmentation needed and
Don't Fragment set</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Administered</td>
<td align="left" colspan="1" rowspan="1">Access-List</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Administered</td>
<td align="left" colspan="1" rowspan="1">Unicast Reverse Path
Forwarding</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Administered</td>
<td align="left" colspan="1" rowspan="1">Discard Route</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Administered</td>
<td align="left" colspan="1" rowspan="1">Policed</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Administered</td>
<td align="left" colspan="1" rowspan="1">Shaped</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Corrupt</td>
<td align="left" colspan="1" rowspan="1">Bad Packet</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Drop</td>
<td align="left" colspan="1" rowspan="1">Corrupt</td>
<td align="left" colspan="1" rowspan="1">Bad Egress Interface</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Delay</td>
<td align="left" colspan="1" rowspan="1">Min</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Delay</td>
<td align="left" colspan="1" rowspan="1">Mean</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Delay</td>
<td align="left" colspan="1" rowspan="1">Max</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
</tbody>
</table>
<t><xref target="symptom_control_plane_actions_table"/> consolidates for
the control plane a list of common symptoms with their actions, reasons
and causess.</t>
<table align="center" anchor="symptom_control_plane_actions_table">
<name slugifiedName="symptom_control_plane_actions">Describing
Symptoms and their Actions, Reason and Cause for Control Plane</name>
<thead>
<tr>
<th align="left" colspan="1" rowspan="1">Action</th>
<th align="left" colspan="1" rowspan="1">Reason</th>
<th align="left" colspan="1" rowspan="1">Cause</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" colspan="1" rowspan="1">Reachability</td>
<td align="left" colspan="1" rowspan="1">Update</td>
<td align="left" colspan="1" rowspan="1">Imported</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Reachability</td>
<td align="left" colspan="1" rowspan="1">Update</td>
<td align="left" colspan="1" rowspan="1">Received</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Reachability</td>
<td align="left" colspan="1" rowspan="1">Withdraw</td>
<td align="left" colspan="1" rowspan="1">Received</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Reachability</td>
<td align="left" colspan="1" rowspan="1">Withdraw</td>
<td align="left" colspan="1" rowspan="1">Peer Down</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Adjacency</td>
<td align="left" colspan="1" rowspan="1">Established</td>
<td align="left" colspan="1" rowspan="1">Peer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Adjacency</td>
<td align="left" colspan="1" rowspan="1">Established</td>
<td align="left" colspan="1" rowspan="1">Link-Layer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Adjacency</td>
<td align="left" colspan="1" rowspan="1">Teared Down</td>
<td align="left" colspan="1" rowspan="1">Peer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Adjacency</td>
<td align="left" colspan="1" rowspan="1">Teared Down</td>
<td align="left" colspan="1" rowspan="1">Link-Layer</td>
</tr>
</tbody>
</table>
<t><xref target="symptom_management_plane_actions_table"/> consolidates
for the management plane a list of common symptoms with their Actions,
Reasons and Causes.</t>
<table align="center" anchor="symptom_management_plane_actions_table">
<name slugifiedName="symptom_management_plane_actions">Describing
Symptoms and their Actions, Reason and Cause for Management
Plane</name>
<thead>
<tr>
<th align="left" colspan="1" rowspan="1">Action</th>
<th align="left" colspan="1" rowspan="1">Reason</th>
<th align="left" colspan="1" rowspan="1">Cause</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" colspan="1" rowspan="1">Interface</td>
<td align="left" colspan="1" rowspan="1">Up</td>
<td align="left" colspan="1" rowspan="1">Link-Layer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Interface</td>
<td align="left" colspan="1" rowspan="1">Down</td>
<td align="left" colspan="1" rowspan="1">Link-Layer</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Interface</td>
<td align="left" colspan="1" rowspan="1">Errors</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Interface</td>
<td align="left" colspan="1" rowspan="1">Discards</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
<tr>
<td align="left" colspan="1" rowspan="1">Interface</td>
<td align="left" colspan="1" rowspan="1">Unknown Protocol</td>
<td align="left" colspan="1" rowspan="1">-</td>
</tr>
</tbody>
</table>
</section>
<section anchor="Semantic_Metadata" title="Semantic Metadata">
<t>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<xref
target="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).</t>
<section anchor="symptom-model-tree"
title="Overview of the Model for the Symptom Semantic Metadata">
<t><xref target="symptom-semantic-metadata-tree"/> contains the YANG
tree diagram <xref target="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. <figure
anchor="symptom-semantic-metadata-tree"
title="YANG tree diagram for ietf-symptom-semantic-metadata">
<artwork><![CDATA[
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
]]></artwork>
</figure></t>
</section>
<section anchor="incident-model-tree"
title="Overview of the Model for the Incident Semantic Metadata">
<t><xref target="incident-semantic-metadata-tree"/> contains the YANG
tree diagram <xref target="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. <figure
anchor="incident-semantic-metadata-tree"
title="YANG tree diagram for ietf-incident-semantic-metadata">
<artwork><![CDATA[
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
]]></artwork>
</figure></t>
</section>
</section>
<section anchor="Implementation" title="Implementation status">
<t>This section provides pointers to existing open source implementations of this draft.
Note to the RFC-editor: Please remove this before publishing.</t>
<section anchor="Implementation-Antagonist" title="Antagonist">
<t>A tool called Antagonist has been implemented
during the IETF 118 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:
<ul>
<li>the generation of a ground truth in relation to symptoms and incidents
in timeseries data</li>
<li>the visual validation of results produced by automated network
anomaly detection tools.</li>
</ul>
The open source code can be found here: <xref target="Antagonist"/>
</t>
</section>
</section>
<section anchor="Security" title="Security Considerations">
<t>The security considerations.</t>
</section>
<section anchor="Acknowledgements" title="Acknowledgements">
<t>The authors would like to thank xxx for their review and valuable
comments.</t>
</section>
</middle>
<back>
<references title="Normative References">
<?rfc include='reference.RFC.2119'?>
<?rfc include='reference.RFC.8174'?>
<?rfc include='reference.RFC.8340'?>
<?rfc include='reference.RFC.9232'?>
<reference anchor="Ahf23"
target="https://anrw23.hotcrp.com/doc/anrw23-paper8.pdf">
<front>
<title>Daisy: Practical Anomaly Detection in large BGP/MPLS and
BGP/SRv6 VPN Networks</title>
<author fullname="Alex Huang Feng" initials="A."
surname="Huang Feng"/>
<date month="July" year="2023"/>
</front>
<seriesInfo name="DOI" value="10.1145/3606464.3606470"/>
<refcontent>IETF 117, Applied Networking Research
Workshop</refcontent>
</reference>
</references>
<references title="Informative References">
<?rfc include='reference.RFC.4364'?>
<?rfc include='reference.RFC.5102'?>
<?rfc include='reference.RFC.7011'?>
<?rfc include='reference.RFC.7270'?>
<?rfc include='reference.RFC.7854'?>
<?rfc include='reference.RFC.8343'?>
<?rfc include='reference.RFC.9418'?>
<?rfc include='reference.I-D.feng-opsawg-incident-management'?>
<?rfc include='reference.I-D.ietf-ippm-pam'?>
<?rfc include='reference.I-D.claise-opsawg-collected-data-manifest'?>
<?rfc include='reference.I-D.ietf-opsawg-ipfix-on-path-telemetry'?>
<reference anchor="VAP09"
target="https://www.researchgate.net/publication/220565847_Anomaly_Detection_A_Survey">
<front>
<title>Anomaly detection: A survey</title>
<author fullname="Varun Chandola" initials="V." surname="Chandola"/>
<author fullname="Arindam Banerjee" initials="A." surname="Banerjee"/>
<author fullname="Vipin Kumar" initials="V." surname="Kumar"/>
<date month="July" year="2009"/>
</front>
<seriesInfo name="DOI" value="10.1145/1541880.1541882"/>
<refcontent>IETF 117, Applied Networking Research
Workshop</refcontent>
</reference>
<reference anchor="Deh22"
target="https://www.oreilly.com/library/view/data-mesh/9781492092384/">
<front>
<title>Data Mesh</title>
<author fullname="Zhamak Dehghani" initials="Z." surname="Dehghani"/>
<date month="March" year="2022"/>
</front>
<seriesInfo name="ISBN" value="9781492092391"/>
<refcontent>O'Reilly Media</refcontent>
</reference>
<reference anchor="Antagonist"
target="https://github.com/vriccobene/antagonist">
<front>
<title>Antagonist: Anomaly tagging on historical data</title>
<author fullname="Vincenzo Riccobene" initials="V." surname="Riccobene"/>
<author fullname="Wanting Du" initials="W." surname="Du"/>
<author fullname="Thomas Graf" initials="T." surname="Graf"/>
<author fullname="Alex Huang Feng" initials="H." surname="Huang Feng"/>
</front>
</reference>
</references>
</back>
</rfc>