Internet DRAFT - draft-cui-savnet-anti-ddos
draft-cui-savnet-anti-ddos
SAVNET Working Group Y. Cui
Internet-Draft J. Wu
Intended status: Informational Tsinghua University
Expires: 5 September 2024 L. Li
L. Zhang
Zhongguancun Laboratory
4 March 2024
SAV-based Anti-DDoS Architecture
draft-cui-savnet-anti-ddos-03
Abstract
Existing SAV schemes can not effectively defend against IP Spoofing
DDoS under incremental deployment. This document proposes SAV-D, a
savnet based distributed defense architecture to enhance SAV's
defense. The main idea of SAV-D is to collect and aggregate more
threat data from existing SAV devices and then distribute crucial
knowledge to widespread devices, thus significantly expanding defense
across the entire network.
Status of This Memo
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 3
3. SAV-D Architecture . . . . . . . . . . . . . . . . . . . . . 4
3.1. SAV Controller . . . . . . . . . . . . . . . . . . . . . 5
3.2. SAV Device . . . . . . . . . . . . . . . . . . . . . . . 6
3.3. Legacy Device . . . . . . . . . . . . . . . . . . . . . . 6
3.4. Victims' Defense . . . . . . . . . . . . . . . . . . . . 7
3.5. Connection Example . . . . . . . . . . . . . . . . . . . 7
3.6. Data transmission . . . . . . . . . . . . . . . . . . . . 8
4. Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5. Scalability . . . . . . . . . . . . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
7. Security Considerations . . . . . . . . . . . . . . . . . . . 9
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
8.1. Normative References . . . . . . . . . . . . . . . . . . 9
8.2. Informative References . . . . . . . . . . . . . . . . . 10
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
Distributed Denial-of-Service (DDoS) attacks have been a persistent
cyber threat, where IP spoofing DDoS is one of the major
contributors. Amplification DDoS typically exploit IP spoofing to
generate large volumes of traffic with small requests, allowing
attackers to overwhelm the target's resources while evading
detection. Some other DDoS attacks (e.g., TCP SYN Flooding
[RFC4987]) also forge source IP addresses in order to drain the
target's resources.
To eliminate IP spoofing, several Source Address Validation (SAV)
schemes have been proposed, such as SAVI[RFC7039], uRPF[RFC3704] and
EFP-uRPF[RFC8704]. However, the defense effectiveness of current SAV
schemes highly depends on the SAV devices' deployment ratio. A large
number of spoofed packets can only be prevented with a significantly
high deployment ratio, but the incremental deployment process is
often slow. According to CAIDA's Spoofer Project[CAIDA], 24.9% of
IPv4 autonomous systems (excluding NAT), and 33.3% of IPv6 autonomous
systems are still spoofable by March 2023. This indicates a limited
SAV deployment, thus the defense effectiveness.
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In the above context, this document offers an SAV-based anti-DDoS
architecture (SAV-D) that incorporates the following advances.
* SAV-honeynet based threat data collection. Each SAV device
functions as a honeypot that does not directly drop spoofed
packets but instead records the spoofing characteristics and sends
them to a centralized control plane.
* Collaborative defense with both SAV and non-SAV devices. The
control plane detects ongoing attacks and generates filtering
rules. These rules are then distributed to both SAV and non-SAV
devices along the attack paths to manipulate malicious traffic.
* Threat information sharing with the victim-end. The control plane
shares attack detection information and IP blocklists with victim-
end defense systems to assist their mitigations.
Through the mechanisms of honeynet, data aggregation and
distribution, SV-D can fully leverage the value of SAV devices and
threat data, resulting in a significant defense improvement.
1.1. 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. Problem Statement
The effectiveness of existing SAV schemes highly relies on the
deployment ratio of devices, which is currently limited. Adversaries
often actively test their bots for plausibility, packet loss, and
amplification benefits. This testing can force the bots to migrate
from SAV domains to non-SAV domains, resulting in fewer spoofed
packets being blocked by SAV devices. Additionally, uRPF and EFP-
uRPF have issues with filtering accuracy in certain scenarios. Some
managers may hesitate to enable SAV due to the probability of
filtering errors. Moreover, SAV can prevent spoofed packets from
being sent out, but it cannot provide protection for the deployers.
The lack of direct benefits may also impede the deployment process.
In this context, there is a strong need to improve the defense
capabilities of current SAV practices.
To achieve the goal, it is essential to consider the following
limitations. Firstly, due to the attack testing, directly dropping
spoofed packets can reduce the possibility of capturing threat data.
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Secondly, in amplification DDoS, the reflected packets sent to
victims have the authentic src-IP, making them unfilterable by SAV
devices. Lastly, although today's SAV mechanism can filter spoofed
packets at local devices, the important threat information they
provide has yet to be fully utilized. If victims were made aware of
the type of spoofing traffic targeting them, they could execute
faster and more accurate countermeasures.
3. SAV-D Architecture
+------------------------------------------------------------+
| Control Plane (SAV Controller) |
+------------------------------------------------------------+
| +--------------+ +----------------+ +--------------+ |
| |Detecting DDoS| |Generating Rules| |Issuing Rules | |
| +--------------+ +----------------+ +--------------+ |
| -\ -\ |
| -/ -/ |
| +----------------+ +--------------+ |
| | Maintain IP | |Sharing Threat| |
| | Blocklists | |Information | |
| +----------------+ +--------------+ ... |
+---------/\-------------------------------------++----------+
|| +-------------------+|
|| |+------------------+|
|| || ||
+---------++-----------------\/--+ +-------------\/----------+
| Data Plane | | Data Plane (Legacy |
| (SAV Devices) | |Devices,Victims' Defense)|
+--------------------------------+ +-------------------------+
| +----------+ +---------------+ | | +---------------+ |
| |Monitoring| | Filtering | | | | Filtering | |
| +----------+ +---------------+ | | +---------------+ |
| +---------------+ | | +---------------+ |
| |Receiving Rules| | | |Receiving Rules| |
| +---------------+ | | +---------------+ |
| ... | | ... |
+--------------------------------+ +-------------------------+
Figure 1: The SAV-based Anti-DDoS Architecture
The proposed SAV-D is shown in Figure 1, whitch can be deployed on
both intra-domain and inter-domain savnet. It introduces a
centralized control plane (i.e., the controller) that connects SAV
devices, legacy devices, and victims' defense systems. The functions
of the controller can be divided into three parts: attack detection,
analysis and defense execution. The controllers can collect spoofing
characteristics from widespread SAV devices (as honeypots) and
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aggregate them for further analysis. From a whole viewpoint, the
controller can detect ongoing attacks and generate filtering rules
for both SAV and non-SAV devices. In addition, the controller can
maintain IP blocklists based on the information reported by SAV
devices, whitch can assist in detectiong DDoS attacks and generating
filtering rules. And then the rules will be distributed to
corresponding devices to perform filtering. Moreover, the controller
will share the attack information with the victims' defense system to
assist in their defense operations.
3.1. SAV Controller
The controller is a logical entity that can be implemented as a
distributed or centralized cluster system. The placement of
controllers may take several factors into consideration, including
latency, resiliency, and load balancing to connected devices.
* To collect spoofing information, the controller will passively
receive the data sent from the certified SAV devices. The
collected spoofing information should include but not limited to
timestamp, 5-tuple (i.e., src-IP, dst-IP, src-port, dst-port, and
protocol), TCP flag, packet size, and amounts. This information
will be readily stored in a database for further analysis.
* To analyze the aggregated statistics, the controller retrieves the
spoofing information periodically (e.g., every 10 seconds). The
spoofed packets are analyzed based on their src-IP to detect
reflection attacks or flooding attacks with certain algorithms. A
large volume of spoofed packets using a specific protocol (e.g.,
NTP, DNS) is a clear indication that the src-IP is being targeted
by reflection attacks. For flooding attacks, the posssible
evidence is a large number of spoofed packets with same target IP
and different source IP. The detection results include the attack
target, type, duration, malicious IP lists, etc. The detection
algorithm should also fully consider the source of the forged
source address packets. SAV devices deployed at different
locations may report different levels of information.
* Generating filtering rules based on detection results is a
straightforward process. Before the reflection, the filtering
rules are based on src-IP and ports. After reflection, the src-IP
is the server's address, and the dst-IP is the victim's address.
Considering the reflected packets are often much larger than
legitimate packets, filtering rules could be generated based on
dst-IP, ports, and packet size. The time required to generate
filtering rules depends on the severity and duration of attacks.
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* Communicating with relevant devices consists of two folds. One
fold is distributing filtering rules to SAV and legacy devices and
receiving feedback from SAV devices. The other fold is to provide
the victim's defense system with attack detection information,
which is essential to efficiently stop the attack traffic. In
addition, the controller may generate more advanced threat
intelligence information, such as geographic distribution
statistics of IP blacklists, attribute statistics of forged IP,
and so on.
3.2. SAV Device
The SAV devices refer to routers or switches that are capable of
validating the source IP address, including SAVI, uRPF, etc.
Compared to simply dropping spoofed packets, SAV devices are required
to selectively allow spoofed packets through if they do not match the
filtering rules. This mechanism can be considered as a SAV-honeynet
that records threat data related to spoofing.
* The SAV device must register it to the controller when being
installed, in which a unique identification number and other
information (e.g., location, management IP address) may needed.
Whenever a spoofed packet is detected, the SAV device will record
its timestamp, 5-tuple, TCP flag, packet size, and so on.
However, only if the spoofed packet matches existing filtering
rules, will the packet be dropped. After a certain interval, the
recorded data will be compressed and sent to the controller.
* Modern devices are generally capable of filtering based on packet
length and counting the number of filtered packets. Upon
receiving filtering rules from the controller, the SAV device must
install them into its data plane. The SAV device also needs to
record the number of packets filtered by each rule. If a rule
filters no packet during some periods, the rule will be
automatically removed to save the rule's space.
3.3. Legacy Device
The commercial routers that are widely deployed in production are
considered to be legacy devices. Access Control List (ACL) is
universally supported in today's routers for packet filtering.
Legacy devices can achieve extensive filtering by simply connecting
their management interface to the controller and receiving the rules.
Since ACLs may vary across legacy devices, filtering rules must be
adapted to meet the specific requirements of each device. The legacy
routers can join the SAV-D system by registering it to the controller
with information similar to the SAV router. Once registered, the
legacy routers can receive the filtering rules from the controller in
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a safe and trusted channel. These rules will be installed into the
data plane. Similar to SAV devices, if a rule filters no packet
during some period, the rule will be automatically removed.
3.4. Victims' Defense
Victim's defense can be a DDoS mitigation system, a dedicated DDoS
defense device, or any system or device that can receive filtering
rules and threat information. The SAV deployers can request access
to the attack detection information related to themselves. The
information includes various details such as the attack target, type,
duration, and malicious IP lists. These details can serve as
auxiliary signals to boost the detection time. In addition, SAV-D
can provide real-time updated IP blocklists, which can be efficiently
used for blocking malicious traffic. In an ideal situation, the
defense system could provide an interface to directly receive the
information and automatically perform corresponding filtering
policies. This mechanism could improve the effectiveness of DDoS
defense and incentivize more SAV deployment.
3.5. Connection Example
+-------------------------------+
+-------+ | +-------+ +-------+ | +-------+
| 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-D system. 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.
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The SAV controller appears as a single entity to the external.
Realizing the full functionality of the SAV controller MAY require
many 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.
3.6. Data transmission
Data transmission includes bidirectional data transmission of control
plane and data plane. The monitoring information of the spoofed src-
IP packets is transmitted from the data plane to the control plane.
Following the existing definition of savnet, the monitoring
information transmission protocol should follow YANG Data Model for
Intra-domain and Inter-domain Source Address Validation. In the
opposite direction, the filtering rules and threat information are
transmitted. The transmission of filtering instructions can be
referred to DOTS Telemetry[RFC8783], whitch describes the
transmission requirements of collaborative filtering instructions.
The threat information includes the attack detection resultant,
victim IP ddress segmant and etc. [RFC9244] and [RFC8783] describe
the transmission requirements for threat information, whitch can be
the candidate protocol.
4. Workflow
The proposed SAV-D architecture can collaboratively defend the IP
spoofing DDoS in a distributed pattern. The typical procedures are
described as follows.
(i). The SAV routers validate and record the characteristics of
spoofed packets, and periodically send this data to the logically
centralized controller, where the global spoofing information is
aggregated.
(ii). Based on the aggregated statistics, the controller can
accurately detect whether there are ongoing IP spoofing attacks with
the help of predefined algorithms.
(iii). Based on the detection results, the controller can generate
defense policies for both SAV and non-SAV devices. The policies
mainly involve filtering rules on 5-tuple and packet size.
(iv). For detected attacks, the defense policies will be distributed
to all SAV and legacy devices. Moreover, the detection results will
also be sent to the victim's defense system.
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(v). The filtering rules will be installed on relevant devices to
block the malicious packets. If a rule filters no packet during some
period, the rule will be automatically removed.
5. Scalability
When there are large amounts of devices introduced into the SAV-D,
the control plane could be implemented with hierarchical structure,
where multiple sub-level controllers are in charge of the devices
inside AS domains. The single top-level controller can exchange
information (i.e., IP spoofing statistics and filtering rules) with
these sub-level controllers. Additionally, a large number of attacks
and filtering rules could introduce another scalability problem. One
possible solution is to prioritize the mitigations of these attacks,
where severe attacks will be tackled first so that the number of
filtering rules will be limited to moderate scope.
6. IANA Considerations
This document includes no request to IANA.
7. Security Considerations
Adversaries may send forged IP spoofing statistics to the control
plane or send forged filtering rules to SAV and legacy devices, which
could cause severe harm to legitimate traffic. To avoid this
situation, the information transmissions of SAV-D could be encrypted
with certification. There could also be attacks directly on the
SAV-D controllers. As common systems, security systems (e.g.,
firewalls) are essential to protect the controllers. In addition,
hot-standby controllers can also significantly improve security and
availability.
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/rfc/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/rfc/rfc8704>.
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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/rfc/rfc4987>.
[RFC7039] Wu, J., Bi, J., Bagnulo, M., Baker, F., and C. Vogt, Ed.,
"Source Address Validation Improvement (SAVI) Framework",
RFC 7039, DOI 10.17487/RFC7039, October 2013,
<https://www.rfc-editor.org/rfc/rfc7039>.
[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/rfc/rfc8783>.
[RFC9244] Boucadair, M., Ed., Reddy.K, T., Ed., Doron, E., Chen, M.,
and J. Shallow, "Distributed Denial-of-Service Open Threat
Signaling (DOTS) Telemetry", RFC 9244,
DOI 10.17487/RFC9244, June 2022,
<https://www.rfc-editor.org/rfc/rfc9244>.
[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/rfc/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/rfc/rfc8174>.
8.2. Informative References
[CAIDA] "State of IP Spoofing", September 2023,
<https://spoofer.caida.org/summary.php>.
Acknowledgements
Thanks to Linbo Hui, Yannan Hu, Wenyong Wang, Shuisong Hu, Haoran Luo
for their contribution to this draft.
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
Linzhe Li
Zhongguancun Laboratory
Beijing, 100094
China
Email: lilz@zgclab.edu.cn
Lei Zhang
Zhongguancun Laboratory
Beijing, 100094
China
Email: zhanglei@zgclab.edu.cn
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