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Network Working Group                                         CT. NGUYEN
Internet-Draft                                                   M. Park
Intended status: Informational                       Soongsil University
Expires: June 5, 2021                                   December 2, 2020

      Detecting Malicious Middleboxes In Service Function Chaining


   This document addresses problems caused by malicious middleboxes and
   proposes a scheme that can detect them in Service Function Chaining
   (SFC) by combining two mechanisms: direct and indirect.  The direct
   mechanism injects a tool into the middleboxes to observe and report
   the state of each middlebox.  In contrast, the indirect mechanism
   creates a probe service chain, which includes trustful middleboxes,
   to investigate the operation of other middleboxes in the network.

Status of This Memo

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   This Internet-Draft will expire on June 5, 2021.

Copyright Notice

   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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   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.  Attack Models . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Methodology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   5.  Detection Methods . . . . . . . . . . . . . . . . . . . . . .   4
     5.1.  Direct Method: Injection of Malicious Detecting Tool  . .   4
     5.2.  Indirect Method: Probe Chain Generation . . . . . . . . .   5
   6.  Informative References  . . . . . . . . . . . . . . . . . . .   5
   Appendix A.  Acknowledgements . . . . . . . . . . . . . . . . . .   6
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   Service Function Chaining (SFC) creates on-demand ordered chains of
   network services (e.g., the load balancer, firewalls, and network
   address translation), and uses the chains to steer the network
   traffic to ensure that the network is agile and effective.  Service
   functions run as middleboxes, which are connected to switches in the
   network, and SFC connects these switches to create the required
   virtual chains.

   Because of the virtual attributes obtained from SDN and NFV, SFC is
   prone to encounter various security vulnerabilities, especially
   malicious middleboxes.  In particular, attackers can modify the
   service functions that run on the middlebox or inject malicious code
   into the middlebox to perform harmful actions.  Malicious middleboxes
   can create various attack types that exploit the weaknesses of both
   SDN and NFV to disrupt the operation and policy of SFC.  With respect
   to the SDN, malicious middleboxes can attack the control and data
   plane by launching distributed denial-of-service (DDoS) attacks,
   abusing computing resources, or incorrectly managing the network
   traffic.  With respect to the NFV, malicious middleboxes can attack
   the infrastructure of other middleboxes, or even user equipment or
   the network by injecting malware, spoofing or sniffing data, carrying
   out denial-of-service (DoS) attacks, misusing shared resources,
   violating the privacy and confidentiality, etc.

   Many countermeasures have been proposed to protect the network from
   these attacks, by either analyzing the network traffic or by
   installing programs in virtual machines (VMs) to collect data
   generated by the hardware to discover the attacks.  However, in the
   SFC environment, these solutions still have limitations and
   vulnerabilities because they only focus on a specific type of attacks

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   and their mechanisms can be detected and compromised by attackers.
   This document proposes a scheme that can detect malicious middleboxes
   in SFC and is able to overcome the limitation of other methods.  The
   proposed scheme functions by two mechanisms: direct and indirect,
   which make it possible to detect attacks launched both from the
   inside and outside of middleboxes.  The direct mechanism injects a
   tool into the middleboxes to observe and report the state of each
   middlebox.  This tool can discover abnormal action by detecting high
   resource consumption processes or determine whether an abnormal
   process is installed.  On the other hand, the indirect mechanism
   creates a probe service chain, which includes trustful middleboxes,
   to investigate the operation of other middleboxes in the network.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

3.  Attack Models

   In a network, a middlebox is typically created with a fresh operating
   system and service functions, except when the middlebox is created
   from a malicious image.  To perform attacks, attackers either need to
   compromise the normal service functions or inject malicious programs
   into middleboxes.  The malicious middleboxes then acquire the ability
   to drop received packets, duplicate them to place additional burden
   on the next-hop middlebox (packet dropping, multiplying attack) or
   forward packets incorrectly to other middleboxes or even to attackers
   (eavesdropping, man-in-the-middle attacks).  Furthermore, malicious
   middleboxes can run redundant processes, which abuses the resources
   of the middlebox and affects the operation of other service functions
   inside the middlebox.  This document focused on solving the following
   attack situations: packet dropping, multiplying, eavesdropping, man-
   in-the-middle, and resource abusing attacks as shown in Figure 1.  We
   assume that controllers, switches, and the connections between them
   are trustful.  Attackers who succeed in gaining access to controllers
   or switches could exploit the network information and destroy all the
   detection and prevention mechanisms.

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  +--------+   50%  +-----------+      +--------+  200%  +-----------+
  | switch |<-------| malicious |      | switch |<-------| malicious |
  |        |------->| middlebox |      |        |------->| middlebox |
  +--------+  100%  +-----------+      +--------+  100%  +-----------+

  (a) Packet dropping attack       (b) Packet multiplying attack

       +----------+                  +----------------------------------+
       | attacker |<------+          | +-----------+       +---------+  |
       +----------+       |          | | malicious |-+     | normal  |  |
               |          |          | |  program  | |-+   | service |  |
  +--------+   |    +-----------+    | +-----------+ | |   | function|  |
  | switch |<--+    | malicious |    |  +------------+ |   +---------+  |
  |        |------->| middlebox |    |   +-------------+                |
  +--------+        +-----------+    +----------------------------------+

  (c) Eavesdropping,               (d) Packet multiplying attack
      man-in-the-middle attack

                          Figure 1: Attack Models

4.  Methodology

   Our proposed scheme creates and injects a malicious tracking tool
   into middleboxes to detect the attacks.  By tracking the device
   information (the number of processes, CPU and memory usage of each
   process, network traffic on a network interface, etc.), the tool can
   detect the above types of attacks.  It contains the following three
   components: (1) Resource Observation Module tracks the number of
   processes and resources consumed by each process and sends the
   results to the Analyzing Module.; (2) Packet Observation Module
   tracks the network traffic on network interfaces (the number of
   packets entering and exiting, transmission latency, etc.) and sends
   the results to the Analyzing Module.; (3) Analyzing Module, which is
   based on the tracking results from other modules, decides and raises
   alarms to the controller about any irregularities in the operation of

5.  Detection Methods

5.1.  Direct Method: Injection of Malicious Detecting Tool

   As mentioned above, a middlebox is normally created or defined with a
   fresh operating system.  If attackers were to modify the service
   function or inject malicious programs into middleboxes to perform
   attacks, this action could leave a trace.  For example, if an
   abnormal process was installed on the middlebox, this process would
   consume a significant amount of CPU and memory because of harmful

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   actions (e.g., packet handling or packet forwarding), which would be
   easy to detect by observing the resource usage of the computer.  On
   the other hand, if the service functions were to be modified to
   launch attacks, this action could also be detected by comparing the
   typical resource consumption between similar middleboxes.  Other
   malicious actions could be discovered by observing the network
   traffic on network interfaces (e.g., packet dropping or multiplying
   attacks).  Our proposed method is designed to inject and run the
   malicious tracking tool on middleboxes to detect the above-mentioned
   types of attacks.  To reduce the overhead for middleboxes, we can run
   this program either in a random period or run it consecutively to
   perform real-time detection.  All of the detection results are sent
   to the controller.

5.2.  Indirect Method: Probe Chain Generation

   In the event of our malicious tracking tool being detected and even
   compromised to spoof the detecting results, our direct method and
   other proposed solutions would not be effective.  We therefore
   decided to use another approach to solve this problem.  We created
   two trustful middleboxes and connected them to another middlebox to
   form a probe service chain.  We also installed a malicious tracking
   tool in the trustful middleboxes to observe the network traffic.
   This approach entails using the malicious tracking tool to analyze
   the network traffic to discover attacks.  The trustful middleboxes
   are regenerated periodically to prevent the potential protection from
   being compromised, and the middlebox under testing is also chosen

   For example, in a chain including Middlebox_1, Middlebox_x, and
   Middlebox_2 in a row, 100 packets are sent from trustful Middlebox_1
   to under testing Middlebox_x.  These packets are intended to be
   processed by the service function inside Middlebox_x and then be
   forwarded to next-hop trustful Middlebox_2.  After a period, if
   $Middlebox_2$ receives only 90 packets (packet dropping attack), or
   150 packets (packet multiplying attack), or receives a packet after a
   longer time than usual, this may be a man-in-the-middle or an
   eavesdropping attack.  The detection results are also sent to the

6.  Informative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", RFC 2119, March 1997.

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Appendix A.  Acknowledgements

   This work was supported by Institute for Information & communications
   Technology Promotion(IITP) grant funded by the Korea government(MSIT)
   (No.2018-0-00254, SDN security technology development).

Authors' Addresses

   Canh Thang Nguyen
   School of Electronic Engineering
   Soongsil University
   369, Sangdo-ro, Dongjak-gu
   Seoul, Seoul  06978
   Republic of Korea

   Phone: +82 2 828 7175
   EMail: nct@ssu.ac.kr

   Minho Park
   School of Electronic Engineering
   Soongsil University
   369, Sangdo-ro, Dongjak-gu
   Seoul, Seoul  06978
   Republic of Korea

   Phone: +82 2 828 7175
   EMail: mhp@ssu.ac.kr

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