Use Cases for DDoS Open Threat Signaling (DOTS) Telemetry
draft-ietf-dots-telemetry-use-cases-01

Document Type Active Internet-Draft (dots WG)
Authors Yuhei Hayashi  , chenmeiling  , Li Su 
Last updated 2020-11-18
Replaces draft-hayashi-dots-telemetry-use-cases
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DOTS                                                          Y. Hayashi
Internet-Draft                                                       NTT
Intended status: Informational                                   M. Chen
Expires: May 22, 2021                                             Li. Su
                                                                    CMCC
                                                       November 18, 2020

       Use Cases for DDoS Open Threat Signaling (DOTS) Telemetry
                 draft-ietf-dots-telemetry-use-cases-01

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
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   This Internet-Draft will expire on May 22, 2021.

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   Copyright (c) 2020 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
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   to this document.  Code Components extracted from this document must

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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  DDoS Mitigation Based on Attack Traffic Bandwidth . . . .   3
       3.1.1.  Mitigating Attack Flow of Top-talker Preferentially .   3
       3.1.2.  Optimal DMS Selection for Mitigation  . . . . . . . .   5
       3.1.3.  Best-path Selection for Redirection . . . . . . . . .   6
       3.1.4.  Short but Extreme Volumetric Attack Mitigation  . . .   8
     3.2.  DDoS Mitigation Based on Attack Type  . . . . . . . . . .   9
       3.2.1.  Selecting Mitigation Technique  . . . . . . . . . . .   9
     3.3.  Setting up for Detection Based on Attack Detail or
           Baseline  . . . . . . . . . . . . . . . . . . . . . . . .  11
       3.3.1.  Supervised Machine Learning of Flow Collector . . . .  11
       3.3.2.  Unsupervised Machine Learning of Flow Collector . . .  12
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   6.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  14
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  14
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  15
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

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 automated.
   To automate DDoS-attack mitigation, multi-vendor components involved
   in DDoS-attack detection and mitigation should cooperate and support
   standard interfaces to communicate.

   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
   [I-D.ietf-dots-signal-channel][I-D.ietf-dots-data-channel].
   Furthermore, DOTS Telemetry enriches the DOTS protocols with various
   telemetry attributes allowing optimal DDoS-attack mitigation

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   [I-D.ietf-dots-telemetry].  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
   attack-mitigation techniques.

2.  Terminology

   The readers should be familiar with the terms defined in [RFC8612]

   In addition, this document uses the following terms:

   Top-talker:  A top N list of attackers who attack the same target or
      targets.  The list is ordered in terms of a two-tuple bandwidth
      such as bps or pps.

   Supervised Machine Learning:  A machine-learning technique that maps
      an input to an output based on example input-output pairs.

   Unsupervised Machine Learning:  Unsupervised Learning is a machine
      learning technique in which the users do not need to supervise the
      model.

3.  Use Cases

   This section describes DOTS-Telemetry use cases that use attributes
   included in DOTS Telemetry specifications.

3.1.  DDoS Mitigation Based on Attack Traffic Bandwidth

3.1.1.  Mitigating Attack Flow of Top-talker Preferentially

   Large-scale DDoS attacks, such as amplification attacks, often occur.
   Some transit providers have to mitigate large-scale DDoS attacks
   using DMS 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 bandwidth is
   more than the available capacity of the DMS.  To enable this, 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.

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   (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

   In this use case, the forwarding nodes always send statistics of
   traffic flow to the flow collectors by using monitoring functions
   such as IPFIX[RFC7011].  When DDoS attacks occur, the flow collectors
   detect attack traffic and send (src_ip, dst_ip, bandwidth)-tuple
   information of the top talker to the orchestrator using the target-
   prefix and top-talkers attribute of DOTS Telemetry.  The orchestrator
   then checks the available capacity of DMS by using a network
   management protocol such as SNMP[RFC3413].  After 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-rule protocols such as BGP Flowspec[RFC5575].

   In this case, the flow collector implements a DOTS client while the
   orchestrator implements a DOTS server.

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3.1.2.  Optimal DMS Selection for Mitigation

   Transit providers, which have a number of DMSs, can deploy the DMSs
   in clustered form.  In the form, they can select DMS to be used to
   mitigate 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 bandwidth of attack traffic,
   capacity of a DMS.  Figure 2 gives an overview of this use case.

   (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 2: Optimal DMS selection for Mitigation

   In this use case, the forwarding nodes always send statistics of
   traffic flow to the flow collectors by using monitoring functions
   such as IPFIX[RFC7011].  When DDoS attacks occur, the flow collectors
   detect attack traffic and send (dst_ip, bandwidth)-tuple information
   to the orchestrator using the target-prefix and total-attack-traffic
   attribute of DOTS Telemetry.  The orchestrator then checks the

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   available capacity of the DMSs by using a network management protocol
   such as SNMP[RFC3413].  After that, the orchestrator chooses optimal
   DMS which each attack traffic should be redirected.  The orchestrator
   then orders forwarding nodes to redirect the attack traffic to the
   optimal DMS by a routing protocol such as BGP[RFC4271].  The
   algorithm of selecting a DMS is out of the scope of this draft.

   In this case, the flow collector implements a DOTS client while the
   orchestrator implements a DOTS server.

3.1.3.  Best-path Selection for Redirection

   A transit-provider network, which adopts a mesh network, has multiple
   paths to convey attack traffic to a DMS.  In this network, 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, total traffic, and total pipe
   capability.  Figure 3 gives an overview of this use case.

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   (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 3: Best-path Selection for Redirection

   In this use case, the forwarding nodes always send statistics of
   traffic flow to the flow collectors by using monitoring functions
   such as IPFIX[RFC7011].  When DDoS attacks occur, the flow collectors
   detect attack traffic and send (dst_ip, bandwidth)-tuple information
   to the orchestrator using a target-prefix and total-attack-traffic
   attribute of DOTS Telemetry.  On the other hands, forwarding nodes
   send bandwidth of total traffic passing the node and total pipe
   capability to the orchestrator using total-traffic and total-pipe-
   capability attributes of DOTS Telemetry.  The orchestrator then
   selects an optimal path to which each attack-traffic flow should be
   redirected.  After that, the orchestrator orders forwarding nodes to
   redirect the attack traffic to the optimal DMS by dissemination of
   flow-specification-rules protocols such as BGP Flowspec[RFC5575].
   The algorithm of selecting a path is out of the scope of this draft.

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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.  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 a 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 and total pipe capability.  Figure 4 gives an overview
   of this use case.

   (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 4: Short but Extreme Volumetric Attack Mitigation

   In this use case, when DDoS attacks occur, the network management
   system receives alerts.  It then sends the target ip address, pipe
   capability of the target's link, and bandwidth of the DDoS attack
   traffic to the administrative system using the target-prefix, total-
   pipe-capability and total-attack-traffic attributes of DOTS
   Telemetry.  After that, the administrative system orders upper
   forwarding nodes to carry out rate-limit all traffic destined to the

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   target based on the pipe capability by the dissemination of the flow-
   specification-rules protocols such as BGP Flowspec[RFC5575].  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).

3.2.  DDoS Mitigation Based on Attack Type

3.2.1.  Selecting Mitigation Technique

   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 5 gives an overview of this use case.

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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 5: DDoS Mitigation Based on Attack Type

   In this use case, the forwarding nodes send statistics of traffic
   flow to the flow collectors by using a monitoring function such as
   IPFIX[RFC7011].  When DDoS attacks occur, the flow collectors detect
   attack traffic and send (dst_ip, attack_type)-tuple information to
   the orchestrator the using vendor-id and attack-id attribute of DOTS
   Telemetry.  The orchestrator then resolves abused port and orders
   forwarding nodes to block the (dst_ip, src_port)-tuple flow of amp
   attack traffic by dissemination of flow-specification-rule protocols
   such as BGP Flowspec[RFC5575].  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 case, the flow collector implements a DOTS client while the
   orchestrator implements a DOTS server.

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3.3.  Setting up for Detection Based on Attack Detail or Baseline

3.3.1.  Supervised Machine Learning of Flow Collector

   DDoS detection based on monitoring functions, such as IPFIX[RFC7011],
   is a lighter weight method of detecting DDoS attacks than DMSs in
   internet transit provider networks.  On the other hand, DDoS
   detection based on the DMSs is a more accurate method of detecting
   attack traffic or DDoS attacks bettr 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 DMSs.  To use such a
   technique, forwarding nodes, flow collector, and a DMS should
   cooperate.  Figure 5 gives an overview of this use case.

                                   +-----------+
                                  +-----------+| 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 6: Training Supervised Machine Learning of Flow Collector

   In this use case, the forwarding nodes always send statistics of
   traffic flow to the flow collectors by using monitoring functions
   such as IPFIX[RFC7011].  When DDoS attacks occur, DDoS orchestration
   use case[I-D.ietf-dots-use-cases] is carried out and the DMS

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   mitigates all attack traffic destined for a target.  The DDoS-
   mitigation system reports the vendor-id, attack-id and top-talker to
   the flow collector using DOTS telemetry.

   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-talker 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.

   In this case, the DMS implements a DOTS client while the flow
   collector implements a DOTS server.

3.3.2.  Unsupervised Machine Learning of Flow Collector

   DMSs can detect DDoS attack traffic, which means DMSs can also
   identify clean traffic.  The aim of this use case is to carry out
   unsupervised machine-learning for anomarly detection according to
   baseline reported by DMSs.  To use such a technique, forwarding
   nodes, flow collector, and a DMS should cooperate.  Figure 7 gives an
   overview of this use case.

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                                 +-----------+
                                +-----------+|
                           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 7: Training Unsupervised Machine Learning of Flow Collector

   In this use case, the forwarding nodes carry out mirroring traffic
   destined a 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 anomarly detection.

   In this case, the DMS implements a DOTS client while the flow
   collector implements a DOTS server.

4.  Security Considerations

   TBD

5.  IANA Considerations

   This document does not require any action from IANA.

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6.  Acknowledgement

   TBD

7.  References

7.1.  Normative References

   [I-D.ietf-dots-telemetry]
              Boucadair, M., Reddy.K, T., Doron, E., chenmeiling, c.,
              and J. Shallow, "Distributed Denial-of-Service Open Threat
              Signaling (DOTS) Telemetry", draft-ietf-dots-telemetry-14
              (work in progress), November 2020.

   [I-D.ietf-dots-use-cases]
              Dobbins, R., Migault, D., Moskowitz, R., Teague, N., Xia,
              L., and K. Nishizuka, "Use cases for DDoS Open Threat
              Signaling", draft-ietf-dots-use-cases-25 (work in
              progress), July 2020.

   [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>.

   [RFC5575]  Marques, P., Sheth, N., Raszuk, R., Greene, B., Mauch, J.,
              and D. McPherson, "Dissemination of Flow Specification
              Rules", RFC 5575, DOI 10.17487/RFC5575, August 2009,
              <https://www.rfc-editor.org/info/rfc5575>.

   [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>.

   [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>.

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7.2.  Informative References

   [I-D.ietf-dots-data-channel]
              Boucadair, M. and T. Reddy.K, "Distributed Denial-of-
              Service Open Threat Signaling (DOTS) Data Channel
              Specification", draft-ietf-dots-data-channel-31 (work in
              progress), July 2019.

   [I-D.ietf-dots-signal-channel]
              Reddy.K, T., Boucadair, M., Patil, P., Mortensen, A., and
              N. Teague, "Distributed Denial-of-Service Open Threat
              Signaling (DOTS) Signal Channel Specification", draft-
              ietf-dots-signal-channel-41 (work in progress), January
              2020.

Authors' Addresses

   Yuhei Hayashi
   NTT
   3-9-11, Midori-cho
   Musashino-shi, Tokyo  180-8585
   Japan

   Email: yuuhei.hayashi@gmail.com

   Meiling Chen
   CMCC
   32, Xuanwumen West
   BeiJing, BeiJing  100053
   China

   Email:
            chenmeiling@chinamobile.com

Hayashi, et al.           Expires May 22, 2021                 [Page 15]
Internet-Draft          DOTS Telemetry Use Cases           November 2020

   Li Su
   CMCC

               32, Xuanwumen West

               BeiJing

               100053

               China

   Email:
             suli@chinamobile.com

Hayashi, et al.           Expires May 22, 2021                 [Page 16]