Internet-Draft DOTS Telemetry Use Cases September 2022
Hayashi, et al. Expires 9 March 2023 [Page]
Workgroup:
DOTS
Internet-Draft:
draft-ietf-dots-telemetry-use-cases-11
Published:
Intended Status:
Informational
Expires:
Authors:
Y. Hayashi
NTT
M. Chen
CMCC
Li. Su
CMCC

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 in particular 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 working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/.

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 9 March 2023.

▲

Table of Contents

1. Introduction

Distributed 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.

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 [RFC9244]. This document presents sample use cases for DOTS Telemetry, which makes concrete overview and purpose described in [RFC9244]: 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], [RFC8903] and [RFC9244].

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 [RFC9244].

The following subsections assume that once the DOTS signal channel is established, DOTS clients proceed with the telemetry setup configuration as detailed in Section 7 of [RFC9244]. In particular, the following telemetry parameters are used: * 'measurement-interval' to define the period during which percentiles are computed. * 'measurement-sample' to define 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 such large-scale DDoS attacks by using DDoS Mitigation Systems (DMSes) with limited resources, which is already deployed in their network. For example, recent reported large DDoS attacks exceeded 1 Tps.

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 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::2/128 and 2001:db8::3/128).

(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 server functionality

Figure 1: Mitigating DDoS Attack Flow of Top-talkers 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::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: An Example of Message Body to Signal Top-Talkers

The forwarding nodes send traffic statistics to the flow collectors using, e.g., IP Flow Information Export (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" DOTS telemetry attributes. The orchestrator, then, checks the available capacity of the DMSes by using a network management protocol, such as Simple Network Management Protocol (SNMP) [RFC3413]. After that, the orchestrator orders the forwarding nodes to redirect as much of the top-talker's traffic to the DMS as possible by dissemination of Flow Specifications relying upon 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. 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.

(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 server functionality

Figure 3: Optimal 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 using, e.g., IPFIX. When DDoS attacks occur, the flow collectors identify the attack traffic and send information of the attack traffic volume to the orchestrator by using the "target-prefix" and "total-attack-traffic" DOTS telemetry attributes. The orchestrator, then, checks the available capacity of the DMSes by using a network management protocol, such as SNMP. After that, the orchestrator selects an optimal DMS to which each attack traffic should be redirected. For example, a simple DMS selection algorithm is to choose a DMS whose available capacity is greater than the "peak-g" atribute indicated in the DOTS telemetry message. The orchestrator orders the appropriate forwarding nodes to redirect the attack traffic to the optimal 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. Best-path Selection for Redirection

A transit provider network has multiple paths to convey an 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.

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 Flowspec
         |           |+      |              |--->   (Redirect)
         +-----------+       +--------------+

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

* C is for DOTS client functionality
* S is for DOTS server functionality

Figure 5: Best-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: An Example of Message Body with Total Attack
                Traffic and Total Traffic

The forwarding nodes send traffic statistics to the flow collectors by using, e.g., IPFIX. When DDoS attacks occur, the flow collectors identify attack traffic and send information of the attack traffic volume to the orchestrator by using a "target-prefix" and "total-attack-traffic" DOTS telemetry attributes. On the other hands, the underlying forwarding nodes send volume of the total traffic passing the node to the orchestrator by using "total-traffic" telemetry attributes. The orchestrator then selects an optimal path to which each attack-traffic flow should be redirected. For example, the simple algorithm of the selection is to choose a path whose available capacity is greater than the "peak-g" 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 optimal DMS by dissemination of Flow Specifications relying upon tools, such as BGP Flowspec.

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. The feature of the attack is that start from zero and go to maximum values in a very short time span, then go back to zero, and back to maximum, repeating in continuous cycles at short intervals. 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 total pipe capacity. Figure 9 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)

            +------------+       +----------------+
e.g.,       | Network    |  DOTS | Administrative |
Alert ----->| Management |C<--->S| System         | e.g., BGP Flowspec
            | System     |       |                |--->   (Rate-Limit)
            +------------+       +----------------+

              +------------+     +------------+ e.g., BGP Flowspec
              | Forwarding |     | Forwarding |<---  (Rate-Limit X bps)
              |   node     |     |   node     |
        Link1 |            |     |            | 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 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 by using the "target-prefix" and "total-attack-traffic" DOTS telemetry attributes. After that, the administrative system orders relevant forwarding nodes to carry out rate-limit all traffic destined to the target based on the pipe capability by the dissemination of the Flow Specifications relying upon tools, such as BGP Flowspec. 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 (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 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 by using IPFIX. It may also be necessary to inspect the Layer 7 information of suspecious packets to detect attacks such as DNS Water Torture Attacks. Such an 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, 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 as below are shared by using the DOTS data channel in advance. Figure 12 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.

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

  (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
  * DNS Amp: DNS Amplification
  * NTP Amp: NTP Amplification

Figure 10: DDoS Mitigation 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":
               "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 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 \
spoof a target's IP address. The attackers abuse vulnerbilities \
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 Type

Attack mappings are shared by using the DOTS data channel in advance (Section 8.1.6 of [RFC9244]). The forwarding nodes send traffic statistics to the flow collectors by using, e.g., IPFIX. When DDoS attacks occur, the flow collectors identify attack traffic and send attack type information to the orchestrator by using "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, e.g. [RFC8955]. Also, the orchestrator orders relevant forwarding nodes to redirect other traffic than the amplification attack traffic by using a routing protocol, such as BGP.

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 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 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 various total traffic percentiles, total attack traffic percentiles and attack detail 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 is for DOTS client functionality
* S is for 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: An 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: An Example of Message Body with Total Traffic,
     Total Attack Traffic Protocol, 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 by using "total-pipe-capacity" telemetry attribute in DOTS telemetry setup.

When DDoS attacks occur, DDoS mitugation orchestration [RFC8903] is carried out in the transit provider. Then, the DDoS mitigation systems reports 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 system, while removing duplicate contents, and sends them to a network administrator by using DOTS telemetry periodically.

During the DDoS mitigation, the orchestrator in the transit provider retrieves link congestion status from the network manager in the enterprise network by using "total-traffic" telemetry attributes. Then, the orchestrator checks whether the DDoS countermeasures are effective or not by comparing the "total-traffic" and the "total-pipe-capacity" 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 than DMSes in internet transit provider networks. On the other hand, 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 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 16 gives an overview of this use case. Figure 17 provides an example of a DOTS telemetry message body that is used to signal various total attack traffic percentiles and attack detail.


                                +-----------+
                               +-----------+| 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 server functionality

Figure 16: Training Supervised Machine Learning of Flow Collectors

{
  "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::2/128"
                },
                {
                  "source-prefix": "2001:db8::3/128"
                }
              ]
            }
          }
        ]
      }
    ]
  }
}

Figure 17: An Example of Message Body with Attack Type
                and top-talkers

The forwarding nodes send traffic statistics to the flow collectors by using, e.g., 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 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. 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 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
[ Dst ] <========================++==============================
                             |   ||       ||
                             |   ||       |+
                             +---||-------+
                                 ||
                                 |/
                       DOTS  +---X--------+
                        --->C| DDoS       |
                             | mitigation |
                             | system     |
                             +------------+

      * C is for DOTS client functionality
      * S is for DOTS server functionality

Figure 18: Training Unsupervised Machine Learning of Flow Collectors

  {
    "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 19: An Example of Message Body with Traffic Baseline

The forwarding nodes carry out mirroring traffic destined IP address. The DMS then identifies "clean" traffic and reports the baseline attributes to the flow collector by 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

DOTS telemetry security considerations are discussed in Section 14 of [RFC9244]. These considerations apply for 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. 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 13 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 does not require any action from IANA.

6. Acknowledgement

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

Thanks to Paul Wouters for the detailed AD review.

7. References

7.1. 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, , <https://www.rfc-editor.org/info/rfc9244>.

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, , <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, , <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, , <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, , <https://www.rfc-editor.org/info/rfc7149>.
[RFC8612]
Mortensen, A., Reddy, T., and R. Moskowitz, "DDoS Open Threat Signaling (DOTS) Requirements", RFC 8612, DOI 10.17487/RFC8612, , <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, , <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, , <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, , <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, , <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, , <https://www.rfc-editor.org/info/rfc9132>.

Authors' Addresses

Yuhei Hayashi
NTT
3-9-11, Midori-cho, Tokyo
180-8585
Japan
Meiling Chen
CMCC
32, Xuanwumen West
BeiJing
BeiJing, 100053
China
Li Su
CMCC
32, Xuanwumen West
BeiJing, BeiJing
100053
China