Internet Engineering Task Force (IETF) Y. Hayashi

Internet Engineering Task Force (IETF) Y. Hayashi
Request for Comments: 9387 NTT
Category: Informational M. Chen
ISSN: 2070-1721 L. Su
China Mobile
April 2023

Use Cases for DDoS Open Threat Signaling (DOTS) Telemetry

Abstract

DDoS Open Threat Signaling (DOTS) telemetry enriches the base DOTS
protocols to assist the mitigator in using efficient DDoS attack
mitigation techniques in a network. This document presents sample
use cases for DOTS telemetry. It discusses what components are
deployed in the network, how they cooperate, and what information is
exchanged to effectively use these techniques.

Status of This Memo

This document is not an Internet Standards Track specification; it is
published for informational purposes.

This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.

Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9387.

Copyright Notice

Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.

This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
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include Revised BSD License text as described in Section 4.e of the
Trust Legal Provisions and are provided without warranty as described
in the Revised BSD License.

Table of Contents

1. Introduction
2. Terminology
3. Telemetry Use Cases
3.1. Mitigation Resources Assignment
3.1.1. Mitigating Attack Flow of Top Talker Preferentially
3.1.2. DMS Selection for Mitigation
3.1.3. Path Selection for Redirection
3.1.4. Short but Extreme Volumetric Attack Mitigation
3.1.5. Selecting Mitigation Technique Based on Attack Type
3.2. Detailed DDoS Mitigation Report
3.3. Tuning Mitigation Resources
3.3.1. Supervised Machine Learning of Flow Collector
3.3.2. Unsupervised Machine Learning of Flow Collector
4. Security Considerations
5. IANA Considerations
6. References
6.1. Normative References
6.2. Informative References
Acknowledgements
Authors' Addresses

1. Introduction

Distributed Denial-of-Service (DDoS) attacks, such as volumetric
attacks and resource-consuming attacks, are critical threats to be
handled by service providers. When such DDoS attacks occur, service
providers have to mitigate them immediately to protect or recover
their services.

For service providers to immediately protect their network services
from DDoS attacks, DDoS mitigation needs to be highly automated. To
that aim, multivendor components involved in DDoS attack detection
and mitigation should cooperate and support standard interfaces.

DDoS Open Threat Signaling (DOTS) is a set of protocols for real-time
signaling, threat-handling requests, and data filtering between the
multivendor elements [RFC9132] [RFC8783]. DOTS telemetry enriches
the DOTS protocols with various telemetry attributes allowing optimal
DDoS attack mitigation [RFC9244]. This document presents sample use
cases for DOTS telemetry to enhance the overview and the purpose
described in [RFC9244]. This document also presents what components
are deployed in the network, how they cooperate, and what information
is exchanged to effectively use attack-mitigation techniques.

2. Terminology

Readers should be familiar with the terms defined in [RFC8612],
[RFC8903], and [RFC9244].

In addition, this document uses the following terms:

Supervised Machine Learning: A machine-learning technique in which
labeled data is used to train the algorithms (the input and output
data are known).

Unsupervised Machine Learning: A machine-learning technique in which
unlabeled data is used to train the algorithms (the data has no
historical labels).

3. Telemetry Use Cases

This section describes DOTS telemetry use cases that use telemetry
attributes included in the DOTS telemetry specification [RFC9244].

The following subsections assume that once the DOTS signal channel is
established, DOTS clients will proceed with the telemetry setup
configuration detailed in Section 7 of [RFC9244]. The following
telemetry parameters are used:

* "measurement-interval" defines the period during which percentiles
are computed.

* "measurement-sample" defines the time distribution for measuring
values that are used to compute percentiles.

3.1. Mitigation Resources Assignment

3.1.1. Mitigating Attack Flow of Top Talker Preferentially

Some transit providers have to mitigate large-scale DDoS attacks
using DDoS Mitigation Systems (DMSes) with limited resources that are
already deployed in their network. For example, recently reported
large DDoS attacks exceeded several Tbps [DOTS_Overview].

This use case enables transit providers to use their DMS efficiently
under volume-based DDoS attacks whose volume is more than the
available capacity of the DMS. To enable this, the attack traffic of
top talkers is redirected to the DMS preferentially by cooperation
among forwarding nodes, flow collectors, and orchestrators.

Figure 1 gives an overview of this use case. Figure 2 provides an
example of a DOTS telemetry message body that is used to signal top
talkers (2001:db8:1::/48 and 2001:db8:2::/48).

(Internet Transit Provider)

+-----------+ +--------------+ SNMP or YANG/NETCONF
IPFIX +-----------+| DOTS | |<---
--->| Flow ||C<-->S| Orchestrator | BGP Flowspec
| collector |+ | |---> (Redirect)
+-----------+ +--------------+

+-------------+
IPFIX +-------------+| BGP Flowspec (Redirect)
<---| Forwarding ||<---
| nodes ||
| || DDoS Attack
[ Target(s) ]<==========================================
| ++=========================[top talker]
| || ++======================[top talker]
+----|| ||----+
|| ||
|| ||
|/ |/
+----x--x----+
| DDoS | SNMP or YANG/NETCONF
| mitigation |<---
| system |
+------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 1: Mitigating Attack Flow of Top Talker Preferentially

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"total-attack-traffic-protocol": [
{
"protocol": 17,
"unit": "megabit-ps",
"mid-percentile-g": "900"
}
],
"attack-detail": [
{
"vendor-id": 32473,
"attack-id": 77,
"start-time": "1645057211",
"attack-severity": "high",
"top-talker":{
"talker": [
{
"source-prefix": "2001:db8:1::/48",
"total-attack-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "100"
}
]
},
{
"source-prefix": "2001:db8:2::/48",
"total-attack-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "90"
}
]
}
]
}
}
]
}
]
}
}

Figure 2: Example of Message Body to Signal Top Talkers

The forwarding nodes send traffic statistics to the flow collectors,
e.g., using IP Flow Information Export (IPFIX) [RFC7011]. When DDoS
attacks occur, the flow collectors identify the attack traffic and
send information about the top talkers to the orchestrator using the
"target-prefix" and "top-talkers" DOTS telemetry attributes. The
orchestrator then checks the available capacity of the DMSes using a
network management protocol, such as the Simple Network Management
Protocol (SNMP) [RFC3413] or YANG with the Network Configuration
Protocol (YANG/NETCONF) [RFC7950]. After that, the orchestrator
orders the forwarding nodes to redirect as much of the top talker's
traffic to the DMSes as they can handle by dissemination of Flow
Specifications using tools such as Border Gateway Protocol
Dissemination of Flow Specification Rules (BGP Flowspec) [RFC8955].

The flow collector implements a DOTS client while the orchestrator
implements a DOTS server.

3.1.2. DMS Selection for Mitigation

Transit providers can deploy their DMSes in clusters. Then, they can
select the DMS to be used to mitigate a DDoS attack at the time of an
attack.

This use case enables transit providers to select a DMS with
sufficient capacity for mitigation based on the volume of the attack
traffic and the capacity of the DMS. Figure 3 gives an overview of
this use case. Figure 4 provides an example of a DOTS telemetry
message body that is used to signal percentiles for total attack
traffic.

(Internet Transit Provider)

+-----------+ +--------------+ SNMP or YANG/NETCONF
IPFIX +-----------+| DOTS | |<---
--->| Flow ||C<-->S| Orchestrator | BGP (Redirect)
| collector |+ | |--->
+-----------+ +--------------+

+------------+
IPFIX +------------+| BGP (Redirect)
<---| Forwarding ||<---
| nodes ||
| || DDoS Attack
[Target A] | ++=================== [Destined for Target A]
[Target B] | || ++=============== [Destined for Target B]
+-||--||-----+
|| ||
++====++ || (congested DMS)
|| || +-----------+
|| |/ | DMS3 |
|| +-----x------+ |<--- SNMP or YANG/NETCONF
|/ | DMS2 |--------+
+--x---------+ |<--- SNMP or YANG/NETCONF
| DMS1 |------+
| |<--- SNMP or YANG/NETCONF
+------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 3: DMS Selection for Mitigation

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"192.0.2.3/32"
]
},
"total-attack-traffic": [
{
"unit": "megabit-ps",
"low-percentile-g": "600",
"mid-percentile-g": "800",
"high-percentile-g": "1000",
"peak-g":"1100",
"current-g":"700"
}
]
}
]
}
}

Figure 4: Example of Message Body with Total Attack Traffic

The forwarding nodes send traffic statistics to the flow collectors,
e.g., using IPFIX. When DDoS attacks occur, the flow collectors
identify the attack traffic and send information about the attack
traffic volume to the orchestrator using the "target-prefix" and
"total-attack-traffic" DOTS telemetry attributes. The orchestrator
then checks the available capacity of the DMSes using a network
management protocol, such as the Simple Network Management Protocol
(SNMP) [RFC3413] or YANG with the Network Configuration Protocol
(YANG/NETCONF) [RFC7950]. After that, the orchestrator selects a DMS
with sufficient capacity to which attack traffic should be
redirected. For example, a simple DMS selection algorithm can be
used to choose a DMS whose available capacity is greater than the
"peak-g" telemetry attribute indicated in the DOTS telemetry message.
The orchestrator orders the appropriate forwarding nodes to redirect
the attack traffic to the DMS relying upon routing policies, such as
BGP [RFC4271].

The detailed DMS selection algorithm is out of the scope of this
document.

The flow collector implements a DOTS client while the orchestrator
implements a DOTS server.

3.1.3. Path Selection for Redirection

A transit provider network has multiple paths to convey attack
traffic to a DMS. In such a network, the attack traffic can be
conveyed while avoiding congested links by adequately selecting an
available path.

This use case enables transit providers to select a path with
sufficient bandwidth for redirecting attack traffic to a DMS
according to the bandwidth of the attack traffic and total traffic.
Figure 5 gives an overview of this use case. Figure 6 provides an
example of a DOTS telemetry message body that is used to signal
percentiles for total traffic and total attack traffic.

(Internet Transit Provider)

+-----------+ +--------------+ DOTS
+-----------+| | |S<---
IPFIX | Flow || DOTS | Orchestrator |
-->| collector ||C<-->S| | BGP Flowspec (Redirect)
| |+ | |--->
+-----------+ +--------------+

DOTS +------------+ DOTS +------------+ IPFIX
--->C| Forwarding | --->C| Forwarding |--->
BGP Flowspec | node | | node |
(Redirect) --->| | | | DDoS Attack
[Target] | ++====================================
+-------||---+ +------------+
|| /
|| / (congested link)
|| /
DOTS +-||----------------+ BGP Flowspec (Redirect)
--->C| || Forwarding |<---
| ++=== node |
+----||-------------+
|/
+--x-----------+
| DMS |
+--------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 5: Path Selection for Redirection

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"total-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "1300",
"peak-g": "800"
}
],
"total-attack-traffic": [
{
"unit": "megabit-ps",
"low-percentile-g": "600",
"mid-percentile-g": "800",
"high-percentile-g": "1000",
"peak-g": "1100",
"current-g": "700"
}
]
}
]
}
}

Figure 6: Example of Message Body with Total Attack Traffic and
Total Traffic

The forwarding nodes send traffic statistics to the flow collectors,
e.g., using IPFIX. When DDoS attacks occur, the flow collectors
identify attack traffic and send information about the attack traffic
volume to the orchestrator using the "target-prefix" and "total-
attack-traffic" DOTS telemetry attributes. The underlying forwarding
nodes send the volume of the total traffic passing the node to the
orchestrator using the "total-traffic" telemetry attributes. The
orchestrator then selects a path with sufficient bandwidth to which
the flow of attack traffic should be redirected. For example, a
simple selection algorithm can be used to choose a path whose
available capacity is greater than the "peak-g" telemetry attribute
that was indicated in a DOTS telemetry message. After that, the
orchestrator orders the appropriate forwarding nodes to redirect the
attack traffic to the DMS by dissemination of Flow Specifications
using tools such as BGP Flowspec [RFC8955].

The detailed path selection algorithm is out of the scope of this
document.

The flow collector and forwarding nodes implement a DOTS client while
the orchestrator implements a DOTS server.

3.1.4. Short but Extreme Volumetric Attack Mitigation

Short but extreme volumetric attacks, such as pulse wave DDoS
attacks, are threats to Internet transit provider networks. These
attacks start from zero and go to maximum values in a very short time
span. The attacks go back to zero and then back to maximum values,
repeating in continuous cycles at short intervals. It is difficult
for transit providers to mitigate such an attack with their DMSes by
redirecting attack flows because this may cause route flapping in the
network. The practical way to mitigate short but extreme volumetric
attacks is to offload mitigation actions to a forwarding node.

This use case enables transit providers to mitigate short but extreme
volumetric attacks. Furthermore, the aim is to estimate the network-
access success rate based on the bandwidth of the attack traffic.
Figure 7 gives an overview of this use case. Figure 8 provides an
example of a DOTS telemetry message body that is used to signal total
pipe capacity. Figure 9 provides an example of a DOTS telemetry
message body that is used to signal various percentiles for total
traffic and total attack traffic.

(Internet Transit Provider)

+------------+ +----------------+
| Network | DOTS | Administrative | BGP Flowspec
Alert----->| Management |C<--->S| System | (Rate-Limit)
| System | | |--->
+------------+ +----------------+
BGP Flowspec
+------------+ +------------+ (Rate-Limit X bps)
| Forwarding | | Forwarding |<---
| node | | node |
Link1 | | | | DDoS & Normal traffic
[Target]<------------------------------------================
Pipe +------------+ +------------+ Attack Traffic
Capability Bandwidth
X bps Y bps

Network-access success rate
X / (X + Y)

C: DOTS client functionality
S: DOTS server functionality

Figure 7: Short but Extreme Volumetric Attack Mitigation

{
"ietf-dots-telemetry:telemetry-setup": {
"telemetry": [
{
"total-pipe-capacity": [
{
"link-id": "link1",
"capacity": "1000",
"unit": "megabit-ps"
}
]
}
]
}
}

Figure 8: Example of Message Body with Total Pipe Capacity

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"total-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "800",
"peak-g": "1300"
}
],
"total-attack-traffic": [
{
"unit": "megabit-ps",
"low-percentile-g": "200",
"mid-percentile-g": "400",
"high-percentile-g": "500",
"peak-g": "600",
"current-g": "400"
}
]
}
]
}
}

Figure 9: Example of Message Body with Total Attack Traffic and
Total Traffic

When DDoS attacks occur, the network management system receives
alerts. Then, it sends the target IP address(es) and volume of the
DDoS attack traffic to the administrative system using the "target-
prefix" and "total-attack-traffic" DOTS telemetry attributes. After
that, the administrative system orders relevant forwarding nodes to
carry out rate-limiting of all traffic destined to the target based
on the pipe capability by the dissemination of the Flow
Specifications using tools such as BGP Flowspec [RFC8955]. In
addition, the administrative system estimates the network-access
success rate of the target, which is calculated by (total-pipe-
capability / (total-pipe-capability + total-attack-traffic)).

Note that total pipe capability information can be gathered by
telemetry setup in advance (Section 7.2 of [RFC9244]).

The network management system implements a DOTS client while the
administrative system implements a DOTS server.

3.1.5. Selecting Mitigation Technique Based on Attack Type

Some volumetric attacks, such as DNS amplification attacks, can be
detected with high accuracy by checking the Layer 3 or Layer 4
information of attack packets. These attacks can be detected and
mitigated through cooperation among forwarding nodes and flow
collectors using IPFIX. It may also be necessary to inspect the
Layer 7 information of suspicious packets to detect attacks such as
DNS water torture attacks [DNS_Water_Torture_Attack]. To carry out
the DNS water torture attack, an attacker commands a botnet to make
thousands of DNS requests for fake subdomains against an
authoritative name server. Such attack traffic should be detected
and mitigated at the DMS.

This use case enables transit providers to select a mitigation
technique based on the type of attack traffic, whether it is an
amplification attack or not. To use such a technique, the attack
traffic is blocked by forwarding nodes or redirected to a DMS based
on the attack type through cooperation among forwarding nodes, flow
collectors, and an orchestrator.

Figure 10 gives an overview of this use case. Figure 11 provides an
example of attack mappings that are shared using the DOTS data
channel in advance. Figure 12 provides an example of a DOTS
telemetry message body that is used to signal percentiles for total
attack traffic, total attack traffic protocol, and total attack
connection; it also shows attack details.

The example in Figure 11 uses the folding defined in [RFC8792] for
long lines.

(Internet Transit Provider)

+-----------+ DOTS +--------------+
+-----------+|<---->| | BGP (Redirect)
IPFIX | Flow ||C S| Orchestrator | BGP Flowspec (Drop)
--->| collector |+ | |--->
+-----------+ +--------------+

+------------+ BGP (Redirect)
IPFIX +------------+| BGP Flowspec (Drop)
<---| Forwarding ||<---
| nodes || DDoS Attack
| ++=====||================
| || ||x<==============[DNS Amp]
| || |+x<==============[NTP Amp]
+-----||-----+
||
|/
+-----x------+
| DDoS |
| mitigation |
| system |
+------------+

C: DOTS client functionality
S: DOTS server functionality
DNS Amp: DNS Amplification
NTP Amp: NTP Amplification

Figure 10: Selecting Mitigation Technique Based on Attack Type

=============== NOTE: '\' line wrapping per RFC 8792 ================

{
"ietf-dots-mapping:vendor-mapping": {
"vendor": [
{
"vendor-id": 32473,
"vendor-name": "mitigator-c",
"last-updated": "1629898958",
"attack-mapping": [
{
"attack-id": 77,
"attack-description": "DNS amplification Attack: \
This attack is a type of reflection attack in which attackers \
spoof a target's IP address. The attackers abuse vulnerabilities \
in DNS servers to turn small queries into larger payloads."
},
{
"attack-id": 92,
"attack-description":"NTP amplification Attack: \
This attack is a type of reflection attack in which attackers \
spoof a target's IP address. The attackers abuse vulnerabilities \
in NTP servers to turn small queries into larger payloads."
}
]
}
]
}
}

Figure 11: Example of Message Body with Attack Mappings

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"total-attack-traffic": [
{
"unit": "megabit-ps",
"low-percentile-g": "600",
"mid-percentile-g": "800",
"high-percentile-g": "1000",
"peak-g": "1100",
"current-g": "700"
}
],
"total-attack-traffic-protocol": [
{
"protocol": 17,
"unit": "megabit-ps",
"mid-percentile-g": "500"
},
{
"protocol": 15,
"unit": "megabit-ps",
"mid-percentile-g": "200"
}
],
"total-attack-connection": [
{
"mid-percentile-l": [
{
"protocol": 15,
"connection": 200
}
],
"high-percentile-l": [
{
"protocol": 17,
"connection": 300
}
]
}
],
"attack-detail": [
{
"vendor-id": 32473,
"attack-id": 77,
"start-time": "1641169211",
"attack-severity": "high"
},
{
"vendor-id": 32473,
"attack-id": 92,
"start-time": "1641172809",
"attack-severity": "high"
}
]
}
]
}
}

Figure 12: Example of Message Body with Total Attack Traffic,
Total Attack Traffic Protocol, Total Attack Connection, and
Attack Detail

Attack mappings are shared using the DOTS data channel in advance
(Section 8.1.6 of [RFC9244]). The forwarding nodes send traffic
statistics to the flow collectors, e.g., using IPFIX. When DDoS
attacks occur, the flow collectors identify attack traffic and send
attack type information to the orchestrator using the "vendor-id" and
"attack-id" telemetry attributes. The orchestrator then resolves
abused port numbers and orders relevant forwarding nodes to block the
amplification attack traffic flow by dissemination of Flow
Specifications using tools such as BGP Flowspec [RFC8955]. Also, the
orchestrator orders relevant forwarding nodes to redirect traffic
other than the amplification attack traffic using a routing protocol,
such as BGP [RFC4271].

The flow collector implements a DOTS client while the orchestrator
implements a DOTS server.

3.2. Detailed DDoS Mitigation Report

It is possible for the transit provider to add value to the DDoS
mitigation service by reporting ongoing and detailed DDoS
countermeasure status to the enterprise network. In addition, it is
possible for the transit provider to know whether the DDoS
countermeasure is effective or not by receiving reports from the
enterprise network.

This use case enables the mutual sharing of information about ongoing
DDoS countermeasures between the transit provider and the enterprise
network. Figure 13 gives an overview of this use case. Figure 14
provides an example of a DOTS telemetry message body that is used to
signal total pipe capacity from the enterprise network administrator
to the orchestrator in the ISP. Figure 15 provides an example of a
DOTS telemetry message body that is used to signal percentiles for
total traffic and total attack traffic as well as attack details from
the orchestrator to the network.

+------------------+ +------------------------+
| Enterprise | | Upstream |
| Network | | Internet Transit |
| +------------+ | | Provider |
| | Network |C | | S+--------------+ |
| | admini- |<-----DOTS---->| Orchestrator | |
| | strator | | | +--------------+ |
| +------------+ | | C ^ |
| | | | DOTS |
| | | S v |
| | | +---------------+ DDoS Attack
| | | | DMS |+=======
| | | +---------------+ |
| | | || Clean |
| | | |/ Traffic |
| +---------+ | | +---------------+ |
| | DDoS | | | | Forwarding | Normal Traffic
| | Target |<================| Node |========
| +---------+ | Link1 | +---------------+ |
+------------------+ +------------------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 13: Detailed DDoS Mitigation Report

{
"ietf-dots-telemetry:telemetry-setup": {
"telemetry": [
{
"total-pipe-capacity": [
{
"link-id": "link1",
"capacity": "1000",
"unit": "megabit-ps"
}
]
}
]
}
}

Figure 14: Example of Message Body with Total Pipe Capacity

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"tmid": 567,
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"target-protocol": [
17
],
"total-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "800"
}
],
"total-attack-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "100"
}
],
"attack-detail": [
{
"vendor-id": 32473,
"attack-id": 77,
"start-time": "1644819611",
"attack-severity": "high"
}
]
}
]
}
}

Figure 15: Example of Message Body with Total Traffic, Total
Attack Traffic, and Attack Detail

The network management system in the enterprise network reports
limits of incoming traffic volume from the transit provider to the
orchestrator in the transit provider in advance. It is reported
using the "total-pipe-capacity" telemetry attribute in the DOTS
telemetry setup.

When DDoS attacks occur, DDoS mitigation orchestration [RFC8903] is
carried out in the transit provider. Then, the DDoS mitigation
systems report the status of DDoS countermeasures to the orchestrator
by sending "attack-detail" telemetry attributes. After that, the
orchestrator integrates the reports from the DDoS mitigation systems,
while removing duplicate contents, and sends the integrated report to
a network administrator using DOTS telemetry periodically.

During the DDoS mitigation, the orchestrator in the transit provider
retrieves the link congestion status from the network manager in the
enterprise network using the "total-traffic" telemetry attributes.
Then, the orchestrator checks whether or not the DDoS countermeasures
are effective by comparing the "total-traffic" and the "total-pipe-
capacity" telemetry attributes.

The DMS implements a DOTS server while the orchestrator behaves as a
DOTS client and a server in the transit provider. In addition, the
network administrator implements a DOTS client.

3.3. Tuning Mitigation Resources

3.3.1. Supervised Machine Learning of Flow Collector

DDoS detection based on tools, such as IPFIX, is a lighter-weight
method of detecting DDoS attacks compared to DMSes in Internet
transit provider networks. DDoS detection based on the DMSes is a
more accurate method for detecting attack traffic than flow
monitoring.

The aim of this use case is to increase flow collectors' detection
accuracy by carrying out supervised machine-learning techniques
according to attack detail reported by the DMSes. To use such a
technique, forwarding nodes, flow collectors, and a DMS should
cooperate. Figure 16 gives an overview of this use case. Figure 17
provides an example of a DOTS telemetry message body that is used to
signal attack detail.

+-----------+
+-----------+| DOTS
IPFIX | Flow ||S<---
--->| collector ||
+-----------++

+------------+
IPFIX +------------+|
<---| Forwarding ||
| nodes || DDoS Attack
[ Target ] | ++==============================
| || ++===========================
| || || ++========================
+---||-|| ||-+
|| || ||
|/ |/ |/
DOTS +---X--X--X--+
--->C| DDoS |
| mitigation |
| system |
+------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 16: Supervised Machine Learning of Flow Collector

{
"ietf-dots-telemetry:telemetry": {
"pre-or-ongoing-mitigation": [
{
"target": {
"target-prefix": [
"2001:db8::1/128"
]
},
"attack-detail": [
{
"vendor-id": 32473,
"attack-id": 77,
"start-time": "1634192411",
"attack-severity": "high",
"top-talker": {
"talker": [
{
"source-prefix": "2001:db8::2/127"
}
]
}
}
]
}
]
}
}

Figure 17: Example of Message Body with Attack Detail and Top Talkers

The forwarding nodes send traffic statistics to the flow collectors,
e.g., using IPFIX. When DDoS attacks occur, DDoS mitigation
orchestration is carried out (as per Section 3.3 of [RFC8903]), and
the DMS mitigates all attack traffic destined for a target. The DDoS
mitigation system reports the "vendor-id", "attack-id", and "top-
talker" telemetry attributes to a flow collector.

After mitigating a DDoS attack, the flow collector attaches outputs
of the DMS as labels to the statistics of the traffic flow of top
talkers. The outputs, for example, are the "attack-id" telemetry
attributes. The flow collector then carries out supervised machine
learning to increase its detection accuracy, setting the statistics
as an explanatory variable and setting the labels as an objective
variable.

The DMS implements a DOTS client while the flow collector implements
a DOTS server.

3.3.2. Unsupervised Machine Learning of Flow Collector

DMSes can detect DDoS attack traffic, which means DMSes can also
identify clean traffic. This use case supports unsupervised machine
learning for anomaly detection according to a baseline reported by
the DMSes. To use such a technique, forwarding nodes, flow
collectors, and a DMS should cooperate. Figure 18 gives an overview
of this use case. Figure 19 provides an example of a DOTS telemetry
message body that is used to signal baseline.

+-----------+
+-----------+|
DOTS | Flow ||
--->S| collector ||
+-----------++

+------------+
+------------+|
| Forwarding ||
| nodes || Traffic
[ Destination ] <== =============++==============================
| || ||
| || |+
+---||-------+
||
|/
DOTS +---X--------+
--->C| DDoS |
| mitigation |
| system |
+------------+

C: DOTS client functionality
S: DOTS server functionality

Figure 18: Unsupervised Machine Learning of Flow Collector

{
"ietf-dots-telemetry:telemetry-setup": {
"telemetry": [
{
"baseline": [
{
"id": 1,
"target-prefix": [
"2001:db8:6401::1/128"
],
"target-port-range": [
{
"lower-port": "53"
}
],
"target-protocol": [
17
],
"total-traffic-normal": [
{
"unit": "megabit-ps",
"low-percentile-g": "30",
"mid-percentile-g": "50",
"high-percentile-g": "60",
"peak-g": "70"
}
]
}
]
}
]
}
}

Figure 19: Example of Message Body with Traffic Baseline

The forwarding nodes carry out traffic mirroring to copy the traffic
destined to an IP address and to monitor the traffic by a DMS. The
DMS then identifies clean traffic and reports the baseline telemetry
attributes to the flow collector using DOTS telemetry.

The flow collector then carries out unsupervised machine learning to
be able to carry out anomaly detection.

The DMS implements a DOTS client while the flow collector implements
a DOTS server.

4. Security Considerations

Security considerations for DOTS telemetry are discussed in
Section 14 of [RFC9244]. These considerations apply to the
communication interfaces where DOTS is used.

Some use cases involve controllers, orchestrators, and programmable
interfaces. These interfaces can be misused by misbehaving nodes to
further exacerbate DDoS attacks. The considerations are for end-to-
end systems for DoS mitigation, so the mechanics are outside the
scope of DOTS protocols. Section 5 of [RFC7149] discusses some
generic security considerations to take into account in such contexts
(e.g., reliable access control). Specific security measures depend
on the actual mechanism used to control underlying forwarding nodes
and other controlled elements. For example, Section 12 of [RFC8955]
discusses security considerations that are relevant to BGP Flowspec.
IPFIX-specific considerations are discussed in Section 11 of
[RFC7011].

5. IANA Considerations

This document has no IANA actions.

6. References

6.1. Normative References

[RFC9244] Boucadair, M., Ed., Reddy.K, T., Ed., Doron, E., Chen, M.,
and J. Shallow, "Distributed Denial-of-Service Open Threat
Signaling (DOTS) Telemetry", RFC 9244,
DOI 10.17487/RFC9244, June 2022,
<https://www.rfc-editor.org/info/rfc9244>.

6.2. Informative References

[DNS_Water_Torture_Attack]
Luo, X., Wang, L., Xu, Z., Chen, K., Yang, J., and T.
Tian, "A Large Scale Analysis of DNS Water Torture
Attack", CSAI '18: Proceedings of the 2018 2nd
International Conference on Computer Science and
Artificial Intelligence, pp. 168-173,
DOI 10.1145/3297156.3297272, December 2018,
<https://dl.acm.org/doi/10.1145/3297156.3297272>.

[DOTS_Overview]
Reddy, T. and M. Boucadair, "DDoS Open Threat Signaling
(DOTS)", July 2020,
<https://datatracker.ietf.org/meeting/108/materials/
slides-108-saag-dots-overview-00>.

[RFC3413] Levi, D., Meyer, P., and B. Stewart, "Simple Network
Management Protocol (SNMP) Applications", STD 62,
RFC 3413, DOI 10.17487/RFC3413, December 2002,
<https://www.rfc-editor.org/info/rfc3413>.

[RFC4271] Rekhter, Y., Ed., Li, T., Ed., and S. Hares, Ed., "A
Border Gateway Protocol 4 (BGP-4)", RFC 4271,
DOI 10.17487/RFC4271, January 2006,
<https://www.rfc-editor.org/info/rfc4271>.

[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>.

[RFC7149] Boucadair, M. and C. Jacquenet, "Software-Defined
Networking: A Perspective from within a Service Provider
Environment", RFC 7149, DOI 10.17487/RFC7149, March 2014,
<https://www.rfc-editor.org/info/rfc7149>.

[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.

[RFC8612] Mortensen, A., Reddy, T., and R. Moskowitz, "DDoS Open
Threat Signaling (DOTS) Requirements", RFC 8612,
DOI 10.17487/RFC8612, May 2019,
<https://www.rfc-editor.org/info/rfc8612>.

[RFC8783] Boucadair, M., Ed. and T. Reddy.K, Ed., "Distributed
Denial-of-Service Open Threat Signaling (DOTS) Data
Channel Specification", RFC 8783, DOI 10.17487/RFC8783,
May 2020, <https://www.rfc-editor.org/info/rfc8783>.

[RFC8792] Watsen, K., Auerswald, E., Farrel, A., and Q. Wu,
"Handling Long Lines in Content of Internet-Drafts and
RFCs", RFC 8792, DOI 10.17487/RFC8792, June 2020,
<https://www.rfc-editor.org/info/rfc8792>.

[RFC8903] Dobbins, R., Migault, D., Moskowitz, R., Teague, N., Xia,
L., and K. Nishizuka, "Use Cases for DDoS Open Threat
Signaling", RFC 8903, DOI 10.17487/RFC8903, May 2021,
<https://www.rfc-editor.org/info/rfc8903>.

[RFC8955] Loibl, C., Hares, S., Raszuk, R., McPherson, D., and M.
Bacher, "Dissemination of Flow Specification Rules",
RFC 8955, DOI 10.17487/RFC8955, December 2020,
<https://www.rfc-editor.org/info/rfc8955>.

[RFC9132] Boucadair, M., Ed., Shallow, J., and T. Reddy.K,
"Distributed Denial-of-Service Open Threat Signaling
(DOTS) Signal Channel Specification", RFC 9132,
DOI 10.17487/RFC9132, September 2021,
<https://www.rfc-editor.org/info/rfc9132>.

Acknowledgements

The authors would like to thank Mohamed Boucadair and Valery Smyslov
for their valuable feedback.

Thanks to Paul Wouters for the detailed AD review.

Many thanks to Donald Eastlake 3rd, Phillip Hallam-Baker, Sean
Turner, and Peter Yee for their reviews.

Thanks to Lars Eggert, Murray Kucherawy, Roman Danyliw, Robert
Wilton, and Éric Vyncke for the IESG review.

Authors' Addresses

Yuhei Hayashi
NTT
3-9-11, Midori-cho, Tokyo
180-8585
Japan
Email: [email protected]

Meiling Chen
China Mobile
32, Xuanwumen West
Beijing
100053
China
Email: [email protected]

Li Su
China Mobile
32, Xuanwumen West
Beijing
100053
China
Email: [email protected]