Use Cases for DDoS Open Threat Signaling (DOTS) Telemetry
draft-ietf-dots-telemetry-use-cases-06
The information below is for an old version of the document.
| Document | Type | Active Internet-Draft (dots WG) | |
|---|---|---|---|
| Authors | Yuhei Hayashi , chenmeiling , Li Su | ||
| Last updated | 2022-02-10 | ||
| Replaces | draft-hayashi-dots-telemetry-use-cases | ||
| Stream | Internet Engineering Task Force (IETF) | ||
| Formats | plain text html xml htmlized pdfized bibtex | ||
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draft-ietf-dots-telemetry-use-cases-06
DOTS Y. Hayashi
Internet-Draft NTT
Intended status: Informational M. Chen
Expires: 17 August 2022 Li. Su
CMCC
13 February 2022
Use Cases for DDoS Open Threat Signaling (DOTS) Telemetry
draft-ietf-dots-telemetry-use-cases-06
Abstract
Denial-of-service 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: 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 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
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 17 August 2022.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
extracted from this document must include Revised BSD License text as
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
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Telemetry Use Cases . . . . . . . . . . . . . . . . . . . . . 3
3.1. Mitigation Resources Assignment . . . . . . . . . . . . . 3
3.1.1. Mitigating Attack Flow of Top-talker
Preferentially . . . . . . . . . . . . . . . . . . . 3
3.1.2. Optimal DMS Selection for Mitigation . . . . . . . . 6
3.1.3. Best-path Selection for Redirection . . . . . . . . . 9
3.1.4. Short but Extreme Volumetric Attack Mitigation . . . 11
3.1.5. Selecting Mitigation Technique Based on Attack
Type . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2. Detailed DDoS Mitigation Report . . . . . . . . . . . . . 18
3.3. Tuning Mitigation Resources . . . . . . . . . . . . . . . 20
3.3.1. Supervised Machine Learning of Flow Collector . . . . 20
3.3.2. Unsupervised Machine Learning of Flow Collector . . . 23
4. Security Considerations . . . . . . . . . . . . . . . . . . . 25
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 25
6. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 25
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.1. Normative References . . . . . . . . . . . . . . . . . . 25
7.2. Informative References . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 26
1. Introduction
Denial-of-Service (DDoS) attacks, such as volumetric attacks and
resource-consumption 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.
Therefore, for service providers to immediately protect their network
services from DDoS attacks, DDoS mitigation needs to be highly
automated. To that aim, multi-vendor components involved in DDoS
attack detection and mitigation should cooperate and support standard
interfaces.
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DDoS Open Threat Signaling (DOTS) is a set of protocols for real-time
signaling, threat-handling requests, and data filtering between the
multi-vendor elements [RFC9132][RFC8783]. DOTS Telemetry enriches
the DOTS protocols with various telemetry attributes allowing optimal
DDoS attack mitigation [I-D.ietf-dots-telemetry]. This document
presents sample use cases for DOTS Telemetry, which makes concrete
overview and purpose described in [I-D.ietf-dots-telemetry]: what
components are deployed in the network, how they cooperate, and what
information is exchanged to effectively use attack-mitigation
techniques.
2. Terminology
The readers should be familiar with the terms defined in [RFC8612]
and [I-D.ietf-dots-telemetry].
In addition, this document uses the following terms:
Top-talker: A list of attack sources that are involved in an attack
and which are generating an important part of the attack traffic.
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 attributes
included in DOTS telemetry specifications [I-D.ietf-dots-telemetry].
3.1. Mitigation Resources Assignment
3.1.1. Mitigating Attack Flow of Top-talker Preferentially
Recent reported large DDoS attacks which exceeded 1 Tps. Some transit
providers have to mitigate such large-scale DDoS attacks using DMSes
(DDoS Mitigation System) with limited resources, which is already
deployed in their network.
The aim of this use case is to enable 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
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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.
(Internet Transit Provider)
+-----------+ +--------------+ e.g., SNMP
e.g., IPFIX +-----------+| DOTS | |<---
--->| Flow ||C<-->S| Orchestrator | e.g., BGP Flowspec
| collector |+ | |---> (Redirect)
+-----------+ +--------------+
+-------------+
e.g., IPFIX +-------------+| e.g., BGP Flowspec
<---| Forwarding ||<--- (Redirect)
| nodes ||
| || DDoS Attack
[ Target ]<============|===============================
[ or ] | ++=========================[top talker]
[ Targets ] | || ++======================[top talker]
+----|| ||---+
|| ||
|| ||
|/ |/
+----x--x----+
| DDoS | e.g., SNMP
| mitigation |<---
| system |
+------------+
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 1: Mitigating DDoS 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,
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"unit": "megabit-ps",
"mid-percentile-g": "900"
}
],
"attack-detail": [
{
"vendor-id": 32473,
"attack-id": 77,
"start-time": "1644539068",
"attack-severity": "high",
"top-talker":{
"talker": [
{
"source-prefix": "2001:db8::2/128",
"total-attack-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "100"
}
]
},
{
"source-prefix": "2001:db8::3/128",
"total-attack-traffic": [
{
"unit": "megabit-ps",
"mid-percentile-g": "90"
}
]
}
]
}
}
]
}
]
}
}
Figure 2: Example of Message Body to Signal Top-Talkers
In this use case, the forwarding nodes send statics of traffic flow
to the flow collectors using, e.g., IPFIX [RFC7011]. When DDoS
attacks occur, the flow collectors identifies the attack traffic and
send information of the top-talkers to the orchestrator using the
"target-prefix" and "top-talkers" telemetry attributes. The
orchestrator, then, checks the available capacity of the DMSes by
using a network management protocol, such as SNMP [RFC3413]. After
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that, the orchestrator orders forwarding nodes to redirect as much of
the top taker's traffic to the DMS as possible by dissemination of
flow-specification-rules relying upon tools, such as BGP Flowspec
[RFC8955].
In this use case, the flow collector implements a DOTS client while
the orchestrator implements a DOTS server.
3.1.2. Optimal 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 under attack
time.
The aim of this use case is to enable transit providers to select an
optimal DMS for mitigation based on the volume of the attack traffic
and the capacity of a 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 various attack traffic percentiles.
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(Internet Transit Provider)
+-----------+ +--------------+ e.g., SNMP
e.g., IPFIX +-----------+| DOTS | |<---
--->| Flow ||C<-->S| Orchestrator | e.g., BGP
| collector |+ | |---> (Redirect)
+-----------+ +--------------+
+------------+
e.g., IPFIX +------------+| e.g., BGP
<---| Forwarding ||<--- (Redirect)
| nodes ||
| || DDoS Attack
[Target] | ++============================
[Target] | || ++========================
+-||--||-----+
|| ||
++====++ || (congested DMS)
|| || +-----------+
|| |/ | DMS3 |
|| +-----x------+ |<--- e.g., SNMP
|/ | DMS2 |--------+
+--x---------+ |<--- e.g., SNMP
| DMS1 |------+
| |<--- e.g., SNMP
+------------+
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 3: Optimal DMS Selection for Mitigation
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{
"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"
}
]
}
]
}
}
Figure 4: Example of Message Body with Total Attack Traffic
In this use case, the forwarding nodes send statics of traffic flow
to the flow collectors using, e.g., IPFIX [RFC7011]. When DDoS
attacks occur, the flow collectors identify attack traffic and send
information of the attack traffic volume to the orchestrator using
the "target-prefix" and "total-attack-traffic" telemetry attributes.
The orchestrator, then, checks the available capacity of the DMSes
using a network management protocol, such as SNMP [RFC3413]. After
that, the orchestrator chooses an optimal DMS to which each attack
traffic should be redirected. [Note: An example how the information
used in the telemetry message is used to trigger the selection will
be written]. The orchestrator then orders the appropriate forwarding
nodes to redirect the attack traffic to the optimal DMS by a routing
protocol such as BGP [RFC4271]. The DMS selection algorithm is out
of the scope of this document.
In this use case, the flow collector implements a DOTS client while
the orchestrator implements a DOTS server.
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3.1.3. Best-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 selecting an available
path.
The aim of this use case is to enable transit providers to select an
optimal path 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 various attack traffic
percentiles and total traffic percentiles.
(Internet Transit Provider)
+-----------+ +--------------+ DOTS
e.g., +-----------+| | |S<---
IPFIX | Flow || DOTS | Orchestrator |
-->| collector ||C<-->S| | e.g., BGP Flow spec
| |+ | |---> (Redirect)
+-----------+ +--------------+
DOTS +------------+ DOTS +------------+ e.g., IPFIX
--->C| Forwarding | --->C| Forwarding |--->
e.g., BGP Flow spec | node | | node |
(Redirect) --->| | | | DDoS Attack
[Target] | ++====================================
+-------||---+ +------------+
|| /
|| / (congested link)
|| /
DOTS +-||----------------+ e.g., BGP Flow spec
--->C| || Forwarding |<--- (Redirect)
| ++=== node |
+----||-------------+
|/
+--x-----------+
| DMS |
+--------------+
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 5: Best-path Selection for Redirection
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{
"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
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In this use case, the forwarding nodes send statics of traffic flow
to the flow collectors using, e.g., IPFIX [RFC7011]. When DDoS
attacks occur, the flow collectors identify attack traffic and send
information of the attack traffic volume to the orchestrator using a
"target-prefix" and "total-attack-traffic" telemetry attributes. On
the other hands, forwarding nodes send volume of the total traffic
passing the node to the orchestrator using "total-traffic" telemetry
attributes. [Note: Should forwarding nodes send the volume of the
total traffic passing the node using telemetry? IPFIX or SNMP is
enough to send ths information.] The orchestrator then selects an
optimal path to which each attack-traffic flow should be redirected.
[Note: An example how this information is used to selection a non-
congested path will be written] After that, the orchestrator orders
the appropriate forwarding nodes to redirect the attack traffic to
the optimal DMS by dissemination of flow-specification-rules relying
upon tools, such as BGP Flowspec [RFC8955]. Path selection algorithm
is out of the scope of this document.
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. [Note:
Pointer of pulse wave DDoS attacks will be written] It is difficult
for them to mitigate an attack by DMS by redirecting attack flows
because it 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.
The aim of this use case is to enable 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
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 various attack traffic percentiles and total traffic
percentiles.
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(Internet Transit Provider)
+------------+ +----------------+
e.g., | Network | DOTS | Administrative |
Alert --->| Management |C<--->S| System | e.g., BGP Flow spec
| System | | |---> (Rate-Limit)
+------------+ +----------------+
+------------+ +------------+ e.g., BGP Flow spec
| Forwarding | | Forwarding |<--- (Rate-Limit X bps)
| node | | node |
| | | | DDoS & Normal traffic
[Target]<------------------------------------================
Pipe +------------+ +------------+ Attack Traffic
Capability Bandwidth
e.g., X bps e.g., Y bps
Network access success rate
e.g., X / (X + Y)
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 7: Short but Extreme Volumetric Attack Mitigation
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{
"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 8: Example of Message Body with Total Attack Traffic and Total Traffic
In this use case, when DDoS attacks occur, the network management
system receives alerts. Then, it sends the target IP address and
volume of the DDoS attack traffic to the administrative system using
the "target-prefix" and "total-attack-traffic" telemetry attributes.
After that, the administrative system orders upper forwarding nodes
to carry out rate-limit all traffic destined to the target based on
the pipe capability by the dissemination of the flow-specification-
rules relying upon 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 gatherd by telemetry
setup in advance. [Note: An example of telemetry pipe setup message
will be written]
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3.1.5. Selecting Mitigation Technique Based on Attack Type
Some volumetric attacks, such as 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[RFC7011]. On the other hand, it is necessary
to inspect the Layer 7 information of attack packets to detect
attacks such as DNS Water Torture Attacks. Such attack traffic
should be detected and mitigated at a DMS.
The aim of this use case is to enable transit providers to select a
mitigation technique based on the type of attack traffic:
amplification attack or not. To use such a technique, attack traffic
is blocked at forwarding nodes or redirected to a DMS based on attack
type through cooperation among forwarding nodes, flow collectors, and
an orchestrator. Figure 9 gives an overview of this use case.
Figure 10 provides an example of a DOTS telemetry message body that
is used to signal various attack traffic percentiles, total traffic
percentiles, total attack connection and attack type.
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(Internet Transit Provider)
+-----------+ DOTS +--------------+ e.g.,
e.g., +-----------+|<---->| | BGP (Redirect)
IPFIX | Flow ||C S| Orchestrator | BGP Flowspec (Drop)
--->| collector |+ | |--->
+-----------+ +--------------+
+------------+ e.g., BGP (Redirect)
e.g., IPFIX +------------+| BGP Flowspec (Drop)
<---| Forwarding ||<---
| nodes || DDoS Attack
| ++=====||================
| || ||x<==============[e.g.,DNS Amp]
| || |+x<==============[e.g.,NTP Amp]
+-----||-----+
||
|/
+-----x------+
| DDoS |
| mitigation |
| system |
+------------+
* C is for DOTS client functionality
* S is for DOTS server functionality
Figure 9: DDoS Mitigation Based on Attack Type
{
"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"
}
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],
"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": "1644539068",
"attack-severity": "high"
},
{
"vendor-id": 32473,
"attack-id": 92,
"start-time": "1644539080",
"attack-severity": "high"
}
]
}
]
}
}
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In this example, attack mappings as below are shared using data-channel in advance.
{
"ietf-dots-mapping:vendor-mapping": {
"vendor": [
{
"vendor-id": 32473,
"vendor-name": "mitigator-c",
"last-updated": "1629898958",
"attack-mapping": [
{
"attack-id": 77,
"attack-description":
"attack-description": "DNS amplification Attack: This attack is a type of reflection attack in which attackers spoofes a target's IP address. The attackers abuses vulnerbilities in DNS servers to turn small queries into larger payloads."
},
{
"attack-id": 92,
"attack-description":
"attack-description":"NTP amplification Attack: This attack is a type of reflection attack in which attackers spoofes a target's IP address. The attackers abuses vulnerbilities in NTP servers to turn small queries into larger payloads."
}
]
}
]
}
}
Figure 10: Example of Message Body with Total Attack Traffic, Total Attack Traffic Protocol, Total Attack Connection and Attack Type
In this use case, the forwarding nodes send statics of traffic flow
to the flow collectors using, e.g., IPFIX [RFC7011]. When DDoS
attacks occur, the flow collectors identify attack traffic and send
attack type information to the orchestrator the using "vendor-id" and
"attack-id" telemetry attributes. The orchestrator then resolves
abused port and orders forwarding nodes to block the amp attack
traffic flow by dissemination of flow-specification-rules relying
upon tools, such as BGP Flowspec [RFC8955]. On the other hand, the
orchestrator orders forwarding nodes to redirect other traffic than
the amp attack traffic by a routing protocol such as BGP [RFC4271].
In this use case, the flow collector implements a DOTS client while
the orchestrator implements a DOTS server.
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3.2. Detailed DDoS Mitigation Report
It is possible for the transit provider to add value to the DDoS
mitigation service by reporting on-going and detailed DDoS
countermeasure status to the enterprise network. In addition, it is
possible for the transit provider to know whether the DDoS counter
measure is effective or not by receiving reports from the enterprise
network.
The aim of this use case is to share the information about on-going
DDoS counter measure between the transit provider and the enterprise
network mutually. Figure 11 gives an overview of this use case.
Figure 12 provides an example of a DOTS telemetry message body that
is used to signal various total traffic percentiles, total attack
traffic percentiles and attack detail.
+------------------+ +------------------------+
| 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 |========
| +---------+ | Link | +---------------+ |
+------------------+ +------------------------+
* C is for DOTS client functionality
* S is for DOTS server functionality
Figure 11: Detailed DDoS Mitigation Report
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{
"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": "1644539068",
"attack-severity": "high"
}
]
}
]
}
}
Figure 12: Example of Message Body with Total Traffic, Total Attack Traffic Protocol and Attack Detail
In this use case, 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 by using "total-pipe-capacity" in DOTS telemetry setup.
[Note: An example of total-pipe-capacity message will be written]
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When DDoS attacks occur, DDoS Orchestration [RFC8903] is carried out
in the transit provider. Then, the DDoS mitigation systems reports
status of DDoS counter measure to the orchestrator sending "attack-
detail" telemetry attributes. After that, the orchestrator
integrates the reports from the DDoS mitigation system, while
removing duplicate contents, and send it to network administrator
using DOTS telemetry periodically.
During the DDoS mitigation, the orchestrator in the transit provider
retrieves link congestion status from the network administrator in
the enterprise network using "total-traffic" telemetry attributes.
[Note: An example of total-traffic message will be written] Then, the
orchestrator checks whether DDoS countermeasure is effective or not
by comparing the "total-traffic" and the "total-pipe-capacity".
In this use case, the DMS implements a DOTS server while the
orchestrator implements a DOTS client and 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 [RFC7011], is a lighter
weight method of detecting DDoS attacks than DMSes in internet
transit provider networks. On the other hand, DDoS detection based
on the DMSes is a more accurate method of detecting attack traffic or
DDoS attacks better than flow monitoring.
The aim of this use case is to increases flow collector's 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 collector, and a DMS should
cooperate. 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 various total attack traffic percentiles and attack detail.
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+-----------+
+-----------+| DOTS
e.g., IPFIX | Flow ||S<---
--->| collector ||
+-----------++
+------------+
e.g., IPFIX +------------+|
<---| Forwarding ||
| nodes || DDoS Attack
[ Target ] | ++==============================
| || ++===========================
| || || ++========================
+---||-|| ||-+
|| || ||
|/ |/ |/
DOTS +---X--X--X--+
--->C| DDoS |
| mitigation |
| system |
+------------+
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 13: Training Supervised Machine Learning of Flow Collector
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{
"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": "1644539068",
"attack-severity": "high",
"top-talker": {
"talker": [
{
"source-prefix": "2001:db8::2/128"
},
{
"source-prefix": "2001:db8::3/128"
}
]
}
}
]
}
]
}
}
Figure 14: Example of Message Body with Attack Type and Top Talkers
In this use case, the forwarding nodes send statics of traffic flow
to the flow collectors using, e.g., IPFIX [RFC7011]. When DDoS
attacks occur, DDoS orchestration use case [RFC8903] is carried out
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 the flow collector.
After mitigating a DDoS attack, the flow collector attaches teacher
labels, which shows normal traffic or attack type, to the statistics
of traffic flow of top-talkers based on the reports. 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.
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In this use case, 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. The aim of this use case is to carry out
unsupervised machine-learning for anomaly detection according to
baseline reported by DMSes. To use such a technique, forwarding
nodes, flow collector, and a DMS should cooperate. Figure 15 gives
an overview of this use case. Figure 16 provides an example of a
DOTS telemetry message body that is used to signal baseline.
+-----------+
+-----------+|
DOTS | Flow ||
--->S| collector ||
+-----------++
+------------+
+------------+|
| Forwarding ||
| nodes || Traffic
[ Dst ] <========================++==============================
| || ||
| || |+
+---||-------+
||
|/
DOTS +---X--------+
--->C| DDoS |
| mitigation |
| system |
+------------+
* C is for DOTS client functionality
* S is for DOTS client functionality
Figure 15: Training Unsupervised Machine Learning of Flow Collector
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{
"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",
"mid-percentile-g": "30",
"mid-percentile-g": "50",
"high-percentile-g": "60",
"peak-g": "70"
}
]
}
]
}
]
}
}
Figure 16: Example of Message Body with Baseline
In this use case, the forwarding nodes carry out mirroring traffic
destined "dst-ip" address. The DMS then identifies "clean" traffic
and reports the baseline 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.
In this use case, the DMS implements a DOTS client while the flow
collector implements a DOTS server.
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4. Security Considerations
DOTS telemetry security considerations are discussed in Section 14 of
[I-D.ietf-dots-telemetry]. This document does not add new
considerations.
5. IANA Considerations
This document does not require any action from IANA.
6. Acknowledgement
The authors would like to thank among others Mohamed Boucadair for
their valuable feedback.
7. References
7.1. Normative References
[I-D.ietf-dots-telemetry]
Boucadair, M., Reddy.K, T., Doron, E., Chen, M., and J.
Shallow, "Distributed Denial-of-Service Open Threat
Signaling (DOTS) Telemetry", Work in Progress, Internet-
Draft, draft-ietf-dots-telemetry-23, 4 February 2022,
<https://www.ietf.org/archive/id/draft-ietf-dots-
telemetry-23.txt>.
7.2. Informative References
[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>.
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[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>.
[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>.
Authors' Addresses
Yuhei Hayashi
NTT
3-9-11, Midori-cho, Tokyo
180-8585
Japan
Email: yuuhei.hayashi@gmail.com
Meiling Chen
CMCC
32, Xuanwumen West
BeiJing
BeiJing, 100053
China
Email: chenmeiling@chinamobile.com
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Li Su
CMCC
32, Xuanwumen West
BeiJing, BeiJing
100053
China
Email: suli@chinamobile.com
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