COINRG I. Fink
Internet-Draft K. Wehrle
Intended status: Informational RWTH Aachen University
Expires: September 12, 2021 March 11, 2021
Enhancing Security and Privacy with In-Network Computing
draft-fink-coin-sec-priv-02
Abstract
With the growing interconnection of devices, cyber security and data
protection are of increasing importance. This is especially the case
regarding cyber-physical systems due to their close entanglement with
the physical world. Misbehavior and information leakage can lead to
financial and physical damage and endanger human lives and well-
being. Thus, hard security and privacy requirements are necessary to
be met. Furthermore, a thorough investigation of incidents is
essential for ultimate protection. Computing in the Network (COIN)
allows the processing of traffic and data directly in the network and
at line-rate. Thus, COIN presents a promising solution for
efficiently providing security and privacy mechanisms as well as
event analysis. This document discusses select mechanisms to
demonstrate how COIN concepts can be applied to counter existing
shortcomings of cyber security and data privacy.
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Copyright Notice
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Protection Mechanisms . . . . . . . . . . . . . . . . . . . . 3
2.1. Encryption and Integrity Checks . . . . . . . . . . . . . 4
2.2. Authorization and Authentication . . . . . . . . . . . . 4
2.3. Behavioral and Enterprise Policies . . . . . . . . . . . 5
2.4. In-Network Vulnerability Patches . . . . . . . . . . . . 6
2.5. Anonymization . . . . . . . . . . . . . . . . . . . . . . 7
3. Intrusion and Anomaly Detection . . . . . . . . . . . . . . . 7
3.1. Intrusion Detection . . . . . . . . . . . . . . . . . . . 7
3.2. Dead Man's Switch . . . . . . . . . . . . . . . . . . . . 8
4. Incident Investigation . . . . . . . . . . . . . . . . . . . 8
5. Security Considerations . . . . . . . . . . . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 9
8. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
With the ongoing digitalization, previously isolated devices and
systems are increasingly connected to the Internet, concerning all
aspects of life. In particular, in the context of Cyber-Physical
Systems (CPS) and the (Industrial) Internet of Things, machines and
infrastructure are equipped with additional sensors and CPUs to allow
for automatization and higher processing efficiency. The
entanglement of the sensors with the physical world leads to high
sensitivity of the transmitted and collected data.
Consequently, digitalization expands the attack surface and the
possible impacts of cyber attacks, increasing the importance of
proper protection mechanisms.
Devices in CPS are often resource-constrained and do not offer the
possibility to implement elaborate security mechanisms. Furthermore,
legacy devices and communication protocols are often still used in
industrial networks but were not designed to face the security and
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privacy challenges the new interconnection brings. Thus,
communication and access are often unprotected. Upgrading legacy
devices with protection mechanisms is an effortful and expensive
procedure. A promising approach for retrofitting security is the
deployment of suitable mechanisms within the network. To date, this
is mainly realized using middle-boxes, leading to overhead and the
need for additional hardware.
One general and widespread security component is Intrusion Detection
Systems (IDS) to detect and, ideally, prevent undesired events in a
network. However, IDS are usually implemented in software, again
running on middle boxes or edge devices in the same network. Thus,
their reaction time is limited as well as their information gain,
which is usually addressed by deploying additional IDS components.
Last, the after-treatment of incidents in networks is critical to
detect exploited vulnerabilities and prevent future attacks. Network
forensics serves to retrace and comprehend the origin and course of
malicious events. However, to provide high performance, the
underlying monitoring of network traffic requires dedicated
networking devices, leading to high costs in traditional networks.
One common problem is that software solutions often require the
deployment of additional hardware and lead to performance overhead,
which is especially unfavorable in the context of time-sensitive
applications, e.g., in industry. Existing high-performance
solutions, e.g., running on traditional networking devices, require
dedicated and costly hardware.
Computing in the Network (COIN) covers these shortfalls by using
programmable networking devices to conduct dynamic and custom
processing of network packets at line-rate. Thus, security-related
functions and packet inspection can be implemented and applied
centrally in the network, e.g., at a programmable switch.
This draft explores the opportunities of COIN for improving security
and privacy as follows: we first describe feasible mechanisms for
preventing attacks and intrusion in the first place. Then, we
present which mechanisms we can implement with COIN for detecting
intrusion and undesired behavior when it has already taken place.
Last, we explore how COIN can improve network forensics for analyzing
and following up incidents, preventing future attacks.
2. Protection Mechanisms
The common ground for providing security and data privacy is to
protect against unauthorized access. That protection is primarily
provided by deploying the basic security mechanisms encryption,
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integrity checking, authentication, and authorization. Those are
especially often missing in resource-constrained environments.
[RFC7744] thoroughly discusses the need for authentication and
authorization in resource-restrained environments. [RFC8576]
presents security and privacy risks and challenges specific to the
IoT. In the following, we describe how COIN can help to retrofit
suitable mechanisms.
2.1. Encryption and Integrity Checks
Encryption is critical to preserve confidentiality when transmitting
data. Integrity checks prevent undetected manipulation, which can
remain unnoticed even despite encryption, e.g., in case of flipped
bits. Due to resource-constraints, many devices in CPS do not
provide encryption or calculation of check-sums.
Complex cryptography is not supported by current programmable
switches either. However, this might change in the future, which
would allow retrofitting encryption and integrity checks at
networking devices. Concretely, using COIN with suitable hardware,
data could be encrypted and supplemented with a check-sum directly at
the first networking device passed by the respective data packet.
The packet is then forwarded through the network or Internet to its
designated destination. Decryption and integrity checks can be
executed at the last networking device before the destination.
Alternatively, this can be implemented at the destination if
supported by the respective device. This approach does not require
deployment or forwarding to additional middle-boxes. Thus, no
additional attack surface or processing overhead is introduced, which
is essential for time-sensitive processes as often at hand in the
industry.
Overall, COIN has the potential to help maintain confidentiality and
integrity efficiently, and thus the availability of resource-
constrained or legacy devices. Questions to clarify are if and at
which costs hardware for enabling cryptographic calculations could
and should be embedded in future generations of programmable
networking devices.
2.2. Authorization and Authentication
Authorization and authentication mechanisms are needed to avoid
unauthorized access to devices and their manipulation in the first
place. With COIN, networking devices can flexibly decide whether to
forward packets, thus enforce authorization and authentication
checks.
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One possibility for authorization is to conduct a handshake between
the sender and networking device before starting the communication
with the industrial device. If not feasible in the networking
hardware, the respective calculations can be conducted in the control
plane. In the case of success, the sender is added to a list of
authorized communication partners. The decision is then enforced by
the networking device. Since authorization is only needed when
starting or refreshing a connection, the necessity and overhead for
consulting the control plane are limited.
The sender can append a secret token for authentication to packets
directed to a specific device. The last networking device in line
can extract the token, authenticate the sender, and forward the
packet in case of success or drop it otherwise. One possibility to
avoid eavesdropping the token is the use of hash chains. Secure
reinitialization can again be done using the control plane, which
usually has the resources for conducting encrypted communication.
In the case of unsuccessful authorization or authentication,
networking devices can inform the network administrator about
possible intrusion of the system.
Undesired traffic can emerge even from authorized and authenticated
devices. A solution is to add policy-based access control, on which
we elaborate in the next subsection.
2.3. Behavioral and Enterprise Policies
Control processes can include communication between various parties.
Even despite authorization and authentication mechanisms, undesired
behavior can occur. For instance, malicious third-party software
might be installed at the approved device. Regarding communication
between two legacy devices, authentication might not be possible at
all. An effective way to exclude malicious behavior nevertheless is
policy-based access control.
[RFC8520] proposes the Manufacturer Usage Description (MUD), a
standard for defining the communication behavior of IoT devices,
which use specific communication patterns. The definition is
primarily based on domain names, ports, and protocols (e.g., TCP and
UDP). Further characteristics as the TLS usage
[I-D.draft-ietf-opsawg-mud-tls-04] or the required bandwidth of a
device [I-D.draft-lear-opsawg-mud-bw-profile-01] can help to define
connections more narrowly.
By defining the typical behavior, we can exclude deviating
communication, including undesired behavior. Likewise to IoT
devices, industrial devices usually serve a specific purpose. Thus,
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the application of MUD or similar policies is possible in industrial
scenarios as well.
The problem that remains to date is the efficient enforcement of such
policies through fine-granular and flexible traffic filtering. While
middle-boxes increase costs and processing overhead, primary SDN
approaches as OpenFlow allow only filtering based on match-action
rules regarding fixed protocol header fields. Evaluation of traffic
statistics for, e.g., limiting the bandwidth, requires consultation
of the remote controller. This leads to latency overheads, which are
not acceptable in time-sensitive scenarios.
In contrast, the COIN paradigm allows flexible filtering even
concerning the content of packets and connection metadata.
Furthermore, traffic filtering can be executed by programmable
networking devices at line-rate.
Going one step further, not only network communication behavior of
devices can be defined in policies. As [KANG] shows, COIN can be
used to consider additional (contextual) parameters, e.g., the time
of day or activity of other devices in the network. Furthermore,
companies can define advanced policies to, e.g., authorize specific
users or subnets.
While the presented policies aim to restrict communication to its
designated purpose, we can use access control to explicitly address
individual devices' security vulnerabilities as described next.
2.4. In-Network Vulnerability Patches
Resource-constrained devices are typically hard to update. Thus,
device vulnerabilities often cannot be fixed after deployment. As a
remedy and special case of policies, rules can be defined to describe
known attacks' signatures. By enforcing these rules at programmable
networking devices, e.g., by dropping matching traffic, COIN offers
an efficient way to avoid exploitation of device vulnerabilities.
Further advantages are the potentially easy and extensive roll-out of
such "in-network patches" in the form of (automatic) software updates
of the networking device.
Future research is needed to evaluate the potential and benefits of
in-network patches compared to traditional security measures, e.g.,
firewalls, and provide proof of concepts using existing devices and
vulnerabilities.
Besides presented security mechanisms, data protection mechanisms are
required to preserve business secrets and the privacy of individuals.
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We show in the following subsection how COIN can contribute to data
anonymization.
2.5. Anonymization
Due to its interconnection with the physical world, the generation of
sensitive data is inherent to CPS. Smart infrastructure leads to the
collection of sensitive user data. In industrial networks,
information about confidential processes is gathered. Such data is
increasingly shared with other entities to increase production
efficiency or enable automatic processing.
Despite the benefits of data exchange, manufacturers and individuals,
might not want to share sensitive information. Again, deployment of
privacy mechanisms is usually not possible at resource-constrained or
legacy devices. COIN has the potential to flexibly apply privacy
mechanisms at line-rate.
Data can be pseudonymized at networking devices by, e.g., extracting
and replacing specific values. Furthermore, elaborate anonymization
techniques can be implemented in the network by sensibly decreasing
the data accuracy. For example, concepts like k-Anonymity can be
applied by aggregating the values of multiple packets before
forwarding the result. Noise addition can be implemented by adding a
random number to values. Similarly, the state-of-the-art technique
differential privacy can be implemented by adding noise to responses
to statistical requests.
Even though the COIN paradigm shows the potential to deploy described
privacy mechanisms within the network, research is needed to clarify
the proposed concepts' feasibility.
3. Intrusion and Anomaly Detection
Ideally, attacks are prevented from the outset. However, in the case
of incidents, fast detection is critical for limiting damage.
Deployment of sensors, e.g., in industrial control systems, can help
to monitor the system state and detect anomalies. This can be used
in combination with COIN to detect intrusion and to provide advanced
safety measures, as described in the following.
3.1. Intrusion Detection
Data of sensors or monitored communication behavior can be compared
against expected patterns to detect intrusion. Even if intrusion
prevention is deployed and connections are allowed when taken
individually, subtle attacks might still be possible. For example, a
series of values might be out of line if put into context even though
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the individual values are unobtrusive. Anomaly detection can be used
to detect such abnormalities and notify the network administrator for
further assessment.
While anomaly detection is usually outsourced to middle-boxes or
external servers, COIN provides the possibility to detect anomalies
at-line rate, e.g., by maintaining statistics about traffic flows.
This decreases costs and latency, which is valuable for a prompt
reaction. Another advantage is that one central networking device
can monitor traffic from multiple devices. In contrast, multiple
distributed middle boxes are usually needed to achieve the same
information gain.
Besides intrusion, anomalies can also imply safety risks. In the
following, we pick up the potential of COIN to support safety.
3.2. Dead Man's Switch
[I-D.draft-irtf-coinrg-use-cases-00] addresses the potential of COIN
for improving industrial safety. Detection of an anomaly in the
sensor data or operational flow can be used to automatically trigger
an emergency shutdown of a system or single system components if the
data indicates an actual hazard. Apart from that, other safety
measures like warning systems or isolation of areas can be
implemented. While we do not aim at replacing traditional dead man's
switches, we see the potential of COIN to accelerate the detection of
failures. Thus, COIN can valuably complement existing safety
measures.
4. Incident Investigation
After detecting an incident, it is essential to conduct Network
Forensics to investigate the origin and spreading of the related
activity. The results of this analysis can be used to allow for
consistent recovery, to adapt protection mechanisms, and prevent
similar events in the future. For enabling potential investigation,
traffic records are constantly collected for each flow in a network,
which requires dedicated hardware in large networks. Furthermore, it
might be preferable to exclude traffic, e.g., from specific subnets,
from the analysis. Dynamic and fine-granular traffic filtering is
not possible with traditional networking devices, leading to storage
and processing overhead.
With COIN, networking devices can be programmed to create flow
records without significant overhead when forwarding a packet.
Furthermore, record generation can be done more flexibly, e.g., by
applying fine-granular traffic filtering. Also, header fields of
particular interest can be efficiently extracted. Therefore, COIN
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can considerably decrease the load and increase the efficiency of
network forensics. This leads, in turn, to a better understanding of
attacks and security.
5. Security Considerations
When implementing security and privacy measures in networking
devices, their security and failure resistance is critical. Related
research questions to clarify in the future are stated in
[I-D.draft-kutscher-coinrg-dir-02].
6. IANA Considerations
N/A
7. Conclusion
COIN has the potential to improve and retrofit security and privacy,
especially with regard to resource-restrained and legacy devices.
First, COIN can provide intrusion prevention mechanisms like
authentication and efficient enforcement of (context-based) policies.
Easily deployable in-network patches of device vulnerabilities could
further improve security. Encryption and integrity checks are
limited by the current hardware but might be realizable in the
future.
Second, COIN allows examining packet contents at networking devices,
which can help implement fast and comprehensive anomaly and intrusion
detection.
Last, COIN can contribute to an efficient and targeted incident
analysis.
Investigation of the feasibility of the presented mechanisms is
subject to future research.
8. Informative References
[I-D.draft-ietf-opsawg-mud-tls-04]
Reddy, T., Wing, D., and B. Anderson, "Manufacturer Usage
Description (MUD) (D)TLS Profiles for IoT Devices", draft-
ietf-opsawg-mud-tls-04 (work in progress), January 2021.
[I-D.draft-irtf-coinrg-use-cases-00]
Kunze, I., Wehrle, K., Trossen, D., and M. Montpetit, "Use
Cases for In-Network Computing", draft-irtf-coinrg-use-
cases-00 (work in progress), February 2021.
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[I-D.draft-kutscher-coinrg-dir-02]
Kutscher, D., Karkkainen, T., and J. Ott, "Directions for
Computing in the Network", draft-kutscher-coinrg-dir-02
(work in progress), July 2020.
[I-D.draft-lear-opsawg-mud-bw-profile-01]
Lear, E. and O. Friel, "Bandwidth Profiling Extensions for
MUD", draft-lear-opsawg-mud-bw-profile-01 (work in
progress), July 2019.
[KANG] Kang, Q., Morrison, A., Tang, Y., Chen, A., and X. Luo,
"Programmable In-Network Security for Context-aware BYOD
Policies", In Proceedings of the 29th USENIX Security
Symposium (USENIX Security 20), August 2020,
<https://www.usenix.org/conference/usenixsecurity20/
presentation/kang>.
[RFC7744] Seitz, L., Ed., Gerdes, S., Ed., Selander, G., Mani, M.,
and S. Kumar, "Use Cases for Authentication and
Authorization in Constrained Environments", RFC 7744,
DOI 10.17487/RFC7744, January 2016,
<https://www.rfc-editor.org/info/rfc7744>.
[RFC8520] Lear, E., Droms, R., and D. Romascanu, "Manufacturer Usage
Description Specification", RFC 8520,
DOI 10.17487/RFC8520, March 2019,
<https://www.rfc-editor.org/info/rfc8520>.
[RFC8576] Garcia-Morchon, O., Kumar, S., and M. Sethi, "Internet of
Things (IoT) Security: State of the Art and Challenges",
RFC 8576, DOI 10.17487/RFC8576, April 2019,
<https://www.rfc-editor.org/info/rfc8576>.
Authors' Addresses
Ina Berenice Fink
RWTH Aachen University
Ahornstr. 55
Aachen D-52062
Germany
Phone: +49-241-80-21419
Email: fink@comsys.rwth-aachen.de
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Klaus Wehrle
RWTH Aachen University
Ahornstr. 55
Aachen D-52062
Germany
Phone: +49-241-80-21401
Email: wehrle@comsys.rwth-aachen.de
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