Internet DRAFT - draft-cui-sada
draft-cui-sada
SAVNET Working Group Y. Cui
Internet-Draft J. Wu
Intended status: Informational Tsinghua University
Expires: 13 March 2023 L. Hui
L. Zhang
Zhongguancun Laboratory
9 September 2022
SAVNET-based Anti-DDoS Architecture
draft-cui-sada-00
Abstract
This document proposes the SAVNET-based Anti-DDoS Architecture
(SADA), which can efficiently detect, mitigate, and traceback Denial-
of-Service (DDoS) attacks that spoof source addresses. The SADA
consists of a distributed DDoS detection mechanism based on
honeynets, a multi-stage DDoS mitigation mechanism, and a suspect-
based DDoS traceback mechanism. By adopting the Source Address
Validation (SAV) technique of SAVNET and introducing the data plane
and the control plane, the SADA makes minor changes to the SAVNET
while providing major benefits.
Status of This Memo
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Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
1.2. Requirements Language . . . . . . . . . . . . . . . . . . 4
2. Architecture . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Distributed DDoS Detection Mechanism Based on
Honeynets . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2. Multi-stage DDoS Mitigation Mechanism . . . . . . . . . . 6
2.3. Suspect-based DDoS Traceback Mechanism . . . . . . . . . 6
2.4. Connection Example . . . . . . . . . . . . . . . . . . . 6
2.5. Establish and Keep Communication . . . . . . . . . . . . 7
3. Data Plane . . . . . . . . . . . . . . . . . . . . . . . . . 8
4. Control Plane . . . . . . . . . . . . . . . . . . . . . . . . 9
5. Incentives for Deployment . . . . . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1. Normative References . . . . . . . . . . . . . . . . . . 10
8.2. Informative References . . . . . . . . . . . . . . . . . 11
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
DDoS attacks using spoofing addresses are notorious on the Internet.
The attackers command a large number of zombie hosts to forge the
target's address and send bogus requests, after which the servers
respond with magnified datagrams to the target, resulting in an
amplification DDoS attacks. Some other DDoS attacks (e.g., TCP SYN
Flooding Attacks [RFC4987]) also forge source IP addresses in order
to drain the target's resources. These DDoS attacks are simple to
carry out but can inflict significant damage. Their attack traffic
is widely dispersed and similar to normal traffic, leading challenge
to detect and mitigate. Furthermore, the spoofed addresses serve as
a mask for the attackers, making it difficult to traceback the
attackers.
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Some Source Address Validation (SAV) techniques have been proposed to
defend against DDoS attacks. The current practice for achieving
ingress filtering is uRPF [RFC3704], which includes strict uRPF and
loose uRPF. Unfortunately, the strict uRPF often improperly blocks
legitimate traffic under asymmetric routing, and the loose uRPF
generally permits all received packets. EFP-uRPF [RFC8704] makes the
uRPF more flexible about directionality, while there are mechanisms
that MAY lead to improper permit or improper block problems in
specific scenarios. The SAVNET Working Group [SAVNET_WG] provides
SAV techniques for intra-domain and inter-domain networks to resolve
the problems raised above. It has been deployed for experimental
practice [RFC5210] and is promising to solve the SAV problem.
However, these SAV techniques are still a long way from being able to
defend against DDoS attacks. First, they only discard spoofing
packets at local devices, lacking coordination to detect DDoS attacks
with a global view. Second, only when these SAV techniques are
widely deployed will they be able to eliminate DDoS attacks using
spoofing addresses, which will take a long time. Third, there are
limited incentives exist to encourage Internet Service Providers
(ISPs) to widely deploy SAV devices.
In the above context, this document offers a SAVNET-based Anti-DDoS
Architecture (SADA) that incorporates the following advances.
* A distributed DDoS detection mechanism based on honeynets. The
SADA introduces a SAV controller for gathering spoofing statistics
from SAV routers that act as honeynets. The SADA can detect DDoS
attacks with a comprehensive analysis using aggregated information
from distributed SAV routers.
* A multi-stage DDoS mitigation mechanism. By overviewing the DDoS
attack with a comprehensive view, the mitigation policies can be
deployed at multiple stages (i.e., near-source, middle, and near-
target). These policies vary at different locations and can
efficiently mitigate the attack.
* A suspect-based DDoS traceback mechanism. The SADA requires SAV
routers to monitor the communication logs of suspicious hosts that
have ever forged addresses. The communication logs will be
analyzed to find the attackers.
The SADA can provide considerable advantages for DDoS attacks by
fully adopting SAVNET features with only minor changes. Even with a
small number of SAV routers deployed, the SADA can deliver accurate
DDoS detections across a larger area. As long as the attack traffic
flows through the SAV domain, the SADA is able to mitigate it. With
the aggregated communication logs of suspicious hosts, the SADA can
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also assist in tracing back the attacker. In addition, the SADA will
provide a spoofing address database and a DDoS attacks database, both
of which will be available for SAV domains and other domains. The
above incentives MAY induce ISPs to widely deploy SAV devices, which
will, in turn, stimulate a more valuable SADA system.
1.1. Terminology
* SADA: the SAVNET-based Anti-DDoS Architecture.
* SAV router: a router that can validate source addresses, make
statistics of suspicious hosts, and execute filtering policies.
* SAV controller: a server that communicates with SAV routers. It
can detect, mitigate, and traceback DDoS attacks.
* SAV device: either a SAV router or a SAV controller.
* SAV domain: a network domain that has SAV routers deployed.
* suspect: a host that ever forged source addresses in the past is
considered a suspect, also called a suspicious host.
* honeynet: consists of SAV routers that record the spoofing
packets' statistics instead of always blocking them.
1.2. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
2. Architecture
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+---------------------------------------------------------------+
| Control Plane (SAV controller) |
+---------------------------------------------------------------+
| +-------------+ +-------------+ +-------------+ |
| | Detection | | Mitigation | | Traceback | |
| +-------------+ +-------------+ +-------------+ |
| +--------------------+ +------------------+ |
| | Spoofing Addresses | | DDoS Attack | |
| | Database | | Database | ... |
| +--------------------+ +------------------+ |
+---------------^-------------------------------+---------------+
| |
Northbound | | Southbound
Interface | | Interface
| |
+---------------+-------------------------------v---------------+
| Data Plane (SAV routers) |
+---------------------------------------------------------------+
| +-------------+ +-------------+ +-------------+ |
| | Monitoring | | Measurement | | Filtering | ... |
| +-------------+ +-------------+ +-------------+ |
+---------------------------------------------------------------+
Figure 1: The SAVNET-based Anti-DDoS Architecture
The proposed SADA is shown in Figure 1. The SADA consists of the
data plane and the control plane, where the primary functions of the
data plane are monitoring, measurement, and filtering, and the
primary functions of the control plane are detecting the attacks,
formulating defense strategies, and tracing back the attacks. The
northbound interface is used to send statistics data to the control
plane, and the southbound interface is used to receive defense
strategies from the control plane. The two planes communicate with
each other and work together to defend against DDoS attacks.
2.1. Distributed DDoS Detection Mechanism Based on Honeynets
The data plane reflects the widely distributed SAV routers that serve
as the architecture's foundation. When detecting packets using
spoofed addresses, the SAV routers do not simply block them but
record their statistics and behaviors, which is regarded as a
honeynet. The SAV routers need periodically transmit the statistics
data to the SAVA controller.
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Based on the statistics data aggregated from the data plane, the
control plane determines whether there is an ongoing DDoS attack.
The judgment MAY refer to the traffic volume, the number of distinct
addresses, the protocol, and the port numbers. A convincing judgment
results include factors such as the ongoing traffic volume, impacted
scope, duration time, and so on.
2.2. Multi-stage DDoS Mitigation Mechanism
The control plane represents the SAV controller, which is the core of
the architecture. With the detailed judgment results, the control
plane then formulates mitigation strategies for multiple stages.
From the spatial perspective, the attack traffic can be divided into
three stages of near-source, middle, and near-target. Mitigation MAY
include various filtering mechanisms on SAV routers at different
stages.
After the mitigation strategies validating by the SAV controller, the
mitigation instructions will be issued to SAV routers. The near-
source SAV routers MAY directly filter the spoofed packets using the
specific forged source address. The middle SAV routers MAY route the
spoofed packets of specific target addresses and protocols into
unreachable destinations. The near-target SAV routers MAY adopt
other filtering techniques to block the malicious packets based on
specific target address, protocol, and packet size. Such a multi-
stage mechanism can mitigate the DDoS attack as much as possible.
2.3. Suspect-based DDoS Traceback Mechanism
The data plane MUST record the communication logs of the suspicious
host that forged source addresses in the past. The communication
logs include the spoofing packets' IP addresses, port numbers, packet
amounts, intervals, frequencies, and so on. These logs will be
periodically transmitted to the SAV controller for further analysis.
When DDoS attacks occur, zombie hosts with spoofing addresses are
potentially communicating with the attackers. Analyzing the
communication logs of these suspicious zombie hosts, the SAV
controller is able to trace back the attacker.
2.4. Connection Example
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+-------------------------------+
+-------+ | +-------+ +-------+ | +-------+
| SR 1 +---+ | SC 1 +----+----+ SC 2 | +--+ SR 3 |
+-------+ | +-------+ | +-------+ | +-------+
| | |
+-------+ | +---+---+ | +-------+
| SR 2 +---+ | SC 3 | +--+ SR 4 |
+-------+ | +-------+ | +-------+
+-------------------------------+
SR: SAV router
SC: SAV controller
Figure 2: Connection Example of SAV Devices
Figure 2 depicts a connection example of SAV devices. There are SAV
routers distributed throughout the network, and they MUST communicate
with the SAV controller in order to collaborate. This document
suggests that each SAV router stores several records of the SAV
controller for backup. Each SAV router MUST try to connect to its
nearest SAV controller at all times. If the SAV router loses contact
with the present controller, it MUST seek the next closest
controller. Such a mechanism can assist SAV routers in maintaining
connections to the best of their abilities.
The SAV controller appears as a single server to the external.
Realizing the full functionality of the SAV controller, it MAY
require much computing and storage resources. As a result, the SAV
controller can be built as clustered or distributed servers, where
consistency and scalability are the primary concerns. Each SAV
controller can communicate with many SAV routers and perform the
corresponding functions.
2.5. Establish and Keep Communication
+------------+ +------------+
| SAV +---------------> SAV |
| Router <---------------+ Controller |
+------------+ +------------+
Figure 3: SAV Router and SAV Controller Establish and Keep
Communications
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Given the broad deployment of SAV routers, each configured SAV router
MUST automatically establish connections with a SAV controller. They
MUST maintain contact after building connections. This document
suggests that an OSPF-like approach be considered. Furthermore, the
SAV router MUST be able to communicate with the SAV controller during
DDoS attacks, and such a mechanism MAY refer to the DOTS Working
Group [DOTS_WG].
3. Data Plane
The data plane is primarily comprised of distributed SAV routers.
SAV routers MAY be deployed in access networks, within Autonomous
System (AS) domains, or at the AS domains boundary. The general
features of SAV routers are the same wherever they are deployed and
can be summarized as follows.
* Collect Spoofing Information. SAV routers need to collect the
statistical data of packets with spoofed addresses. The
information includes but is not limited to spoofed source
addresses, destination addresses, port numbers, packet intervals,
and frequencies.
* Collect information of suspicious hosts. When SAV routers detect
a host forging source addresses, they MAY add the host to the list
of suspicious hosts. The SAV routers MUST then monitor the
communication logs of these suspicious hosts. The logs contain
information that includes but is not limited to destination
addresses, protocols, packet intervals, and frequencies.
* Receive and Execute Instructions. When the SAV controller issue
the defense strategies, the SAV routers MUST respond
appropriately. The response mainly consists of Access Control
Lists (ACL) filtering [RFC8519] and black-hole routing [RFC5635].
SAV routers with various locations will perform different actions,
such as filtering spoofed packets at the access network and black-
hole routing at the AS domain boundary.
* Keep the Capacity for Escape. Attack traffic can sometimes exceed
router links, resulting in disconnection from SAV routers to the
SAV controller. To avoid the terrible circumstance, SAV routers
MUST reserve a specified amount of bandwidth to maintain a
continuous connection with the SAV controller.
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4. Control Plane
The control plane consists of the SAV controller that can be
clustered or distributed servers. The SAV controller are responsible
for detecting, mitigating, and tracing back DDoS attacks. They also
provide spoofing address database and DDoS attacks database for
others to reference. The following are the features of the SAV
controller.
* Aggregate Spoofing Information. The SAV controller collects
spoofing statistics from SAV routers everywhere and aggregates
them for further analysis.
* Detect DDoS Attacks. The SAV controller MUST determine whether a
DDoS attack is ongoing based on the aggregated information. The
judgment results MUST specify the attack target, traffic volume,
impacted scope, duration time, and so on.
* Formulate Defense Strategies. Based on judgment results, the SAV
controller will devise the appropriate defense strategies. The
defense mechanisms MAY include ACL-based filtering and black-hole
routing, which vary on specific SAV routers according to their
locations. The SAV controller then issues detailed defense
instructions to individual SAV routers for execution.
* traceback Attacks. The SAV controller also aggregates information
about suspicious hosts and analyzes the communication logs of
these suspicious hosts to locate the attacker.
* Build and Maintain the databases. The SAV controller MUST build
and maintain the spoofing address database at a global view, which
will, in turn, help to detect DDoS attacks. The SAV controller
MUST also build the DDoS attack database with the detection
results, which contain the details about each attack, such as the
attacker address, the target address, traffic volume, impacted
scope, and duration time. Such a DDoS attack database will help
to review the entire process of attacks.
* Provide Management Interface. Detecting, mitigating, and tracing
back DDoS attacks MAY necessitate some manual settings in certain
contexts. The management interface provides a convenient way to
adjust these settings.
5. Incentives for Deployment
* Provide DDoS Defense Ability. Whenever the attack traffic flows
through the SAV domains, the SAV devices can react to mitigate the
attack. Any ISP that has deployed SAV devices can also obtain the
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spoofing address information and DDoS attacks information. With
this accurate and real-time information, ISPs can decide how to
take measures to protect their customers.
* Locate the Malicious Hosts and Reduce Costs. With deployed SAV
devices, ISPs can identify the zombie hosts and help to traceback
the attackers. These zombie hosts MAY incur additional traffic
and energy costs. Locating and removing these malicious hosts not
only help to reduce the costs but also improve the reputation of
ISPs.
6. IANA Considerations
This document includes no request to IANA.
7. Security Considerations
* When DDoS attacks appear, the SAV routers MAY perform different
filtering policies at different locations. If SAV routers get a
bogus mitigation policy, they MAY undertake destructive filtering
activities.
* The SAV controller is the core of the SADA and MUST be secure at
all times. The SAV controller SHOULD be able to defend themselves
against any invasions.
* The SAV controller's functions are based on statistical data
aggregated from the SAV routers. Fake statistical data might have
unanticipated consequences.
8. References
8.1. Normative References
[RFC3704] Baker, F. and P. Savola, "Ingress Filtering for Multihomed
Networks", BCP 84, RFC 3704, DOI 10.17487/RFC3704, March
2004, <https://www.rfc-editor.org/info/rfc3704>.
[RFC8704] Sriram, K., Montgomery, D., and J. Haas, "Enhanced
Feasible-Path Unicast Reverse Path Forwarding", BCP 84,
RFC 8704, DOI 10.17487/RFC8704, February 2020,
<https://www.rfc-editor.org/info/rfc8704>.
[RFC5210] Wu, J., Bi, J., Li, X., Ren, G., Xu, K., and M. Williams,
"A Source Address Validation Architecture (SAVA) Testbed
and Deployment Experience", RFC 5210,
DOI 10.17487/RFC5210, June 2008,
<https://www.rfc-editor.org/info/rfc5210>.
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[RFC4987] Eddy, W., "TCP SYN Flooding Attacks and Common
Mitigations", RFC 4987, DOI 10.17487/RFC4987, August 2007,
<https://www.rfc-editor.org/info/rfc4987>.
[RFC8519] Jethanandani, M., Agarwal, S., Huang, L., and D. Blair,
"YANG Data Model for Network Access Control Lists (ACLs)",
RFC 8519, DOI 10.17487/RFC8519, March 2019,
<https://www.rfc-editor.org/info/rfc8519>.
[RFC5635] Kumari, W. and D. McPherson, "Remote Triggered Black Hole
Filtering with Unicast Reverse Path Forwarding (uRPF)",
RFC 5635, DOI 10.17487/RFC5635, August 2009,
<https://www.rfc-editor.org/info/rfc5635>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
8.2. Informative References
[SAVNET_WG]
"Source Address Validation in Intra-domain and Inter-
domain Networks", June 2022,
<https://datatracker.ietf.org/doc/charter-ietf-savnet/>.
[DOTS_WG] "DDoS Open Threat Signaling (dots)", March 2022,
<https://datatracker.ietf.org/doc/charter-ietf-dots/>.
Acknowledgements
TBD
Authors' Addresses
Yong Cui
Tsinghua University
Beijing, 100084
China
Email: cuiyong@tsinghua.edu.cn
URI: http://www.cuiyong.net/
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Jianping Wu
Tsinghua University
Beijing, 100084
China
Email: jianping@cernet.edu.cn
Linbo Hui
Zhongguancun Laboratory
Beijing, 100094
China
Email: huilb@zgclab.edu.cn
Lei Zhang
Zhongguancun Laboratory
Beijing, 100094
China
Email: zhanglei@zgclab.edu.cn
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