Problem Statement for Internet of Things Sensing
draft-hsothers-iotsens-ps-00
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draft-hsothers-iotsens-ps-00
Network Working Group D. von Hugo
Internet-Draft Deutsche Telekom
Intended status: Standards Track B. Sarikaya
Expires: 21 April 2022 18 October 2021
Problem Statement for Internet of Things Sensing
draft-hsothers-iotsens-ps-00
Abstract
The document attempts to establish hardware based Internet of Things
authentication as a future networking area beyond 5G going into 6G
for standardization. The problem of hardware authentication is
discussed and its relationship with Wireless Local Area network
collaborative and/or multi-band sensing is established and then
recent research efforts in the area are indicated.
Status of This Memo
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This Internet-Draft will expire on 21 April 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Conventions and Terminology . . . . . . . . . . . . . . . 3
2. Hardware Based Authentication . . . . . . . . . . . . . . . . 4
3. State of the Academic Approaches to IoT Authentication . . . 5
4. IoT Authentication Protocols . . . . . . . . . . . . . . . . 6
5. Hardware IoT Authentication Problem . . . . . . . . . . . . . 6
5.1. Architectural and Procedural Issues for Future IP-based
IoT-Authentication . . . . . . . . . . . . . . . . . . . 7
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
7. Security Considerations . . . . . . . . . . . . . . . . . . . 8
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 8
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
9.1. Normative References . . . . . . . . . . . . . . . . . . 8
9.2. Informative References . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
Future networking to make full use of 5G capabilities or even
resembling an evolution to beyond 5G will have to exploit a much more
heterogeneous environment in terms of network and device connectivity
technologies and applications. In addition ease of use for customers
and human-independent operation of a multitude of devices and
machines (things) has to be provided.
Therefore current authentication models like 802.1X [IEEE802.1X]
which are based on human intervention do not fit well. Also this
model does not scale well for the Internet of Things (IoT). What we
need is hardware based admission model. Such a model will enable
many new applications as we explain more in this document.
IEEE 802.11 [IEEE802.11] has a project on Wireless LAN (WLAN sensing)
and 802.11bf task group (TG) in charge of this project [BFSFD]. Use
cases for 802.11bf TG includes room sensing, i.e., presence
detection, counting the number of people in the room, localization of
active people, audio with user detection, gesture recognition at
different ranges, device proximity detection, home appliance control.
There are also health care related use cases like breathing/heart
rate detection, surveillance of persons of interest, building a 3D
picture of an environment, as, e.g., in-car sensing for driver
sleepiness detection [BFUseCases].
Hardware based authentication that we address in this document builds
on similar use cases. We can summarize the use cases we are
currently considering here: Authenticating the device that is playing
a melody, or a person has just touched; authenticating devices, i.e.
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smart teapot with certain manifests, like blinking red and blue;
authenticate the device when a camera is pointed at it; and the like
[Henning]. 802.11bf sensing project provides proper framework for
hardware based authentication because 802.11 or Wi-Fi devices are
more and more diverse spanning from personal computers, smartphones,
televisions, tablets, and all sorts of IoT devices or sensors.
TGbf is also working on Specification Framework Document with an
outline of each of the functional blocks that will be a part of the
final amendment like wireless LAN sensing procedure [BFSFD]. TGbf
sensing is based on obtaining physical Channel State Information
(CSI) measurements between a transmitter and receiver WLAN nodes,
called stations (STA). Using these measurements, presence of
obstacles between a transmitter and receiver can be detected and
tracked. This way, using feature extraction and classification
provided by means of artificial intelligence (AI), more higher level
tasks like human activity recognition and object detection are
available for authentication purposes, while hardware based
authentication use cases can be achieved through computation of phase
differences, etc.
TGbf Wi-Fi Sensing (SENS) is achieved by signaling between just an
initiator and a responder. TGbf may also define more effective
collaborative SENS (in short, CSENS) where multiple SENS-enabled
devices can collaborate as a group in an orderly fashion to capture
additional information about the surrounding environment [Rest21].
1.1. Conventions and Terminology
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.
Sensing (SENS) is defined as the usage of received Wi-Fi signals from
a Station (STA) to detect features (i.e., range, velocity, angular,
motion, presence or proximity, gesture, etc.) of intended targets
(i.e., object, human, animal, etc.) in a given environment (i.e.,
house, office, room, vehicle, enterprise, etc.).
Collaborative sensing (CSENS) defines the operation in which multiple
SENS enabled devices can collaborate as a group in an orderly fashion
to capture additional information about the surrounding environment
and allow for more precise detection, thus enabling a more reliable
authentication.
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Multi-band sensing is defined as sensing using both sub-7-GHz Channel
State Information (CSI) measurements that provide indication of
relatively large motions and that can propagate through obstacles
(e.g., walls) and 60-GHz Received Signal Strength Indicator (RSSI)
measurements at mmWave that provide highly-directional information
through the usage of beamforming toward a given receiver, but have
small range due to the presence of blockers (e.g., walls).
2. Hardware Based Authentication
Aim of this document is to lay ground for the need for new
authentication models in the framework of devices (e.g., machines in
IoT communication) within a (wireless or wireline-based) network.
Currently employed authentication models (such as e.g., 802.1X
certificate model) is based on a human being using the machine and
providing credentials (e.g., user name/password or a permitted
digital certificate) to the authenticator. Similarly, for user
equipment (UE) to access a cellular network the device has to be
equipped with a USIM and the user has to provide a secret key, i.e.,
PIN (Personal Identification Number). With the use case of massive
IoT (mIoT) as foreseen, e.g., in 5G and with an increasing amount of
devices within a household (smart home) and/or in the ownership of a
customer (smart watch etc.) the need for an ease-of-use hardware-
based admission model arises.
Focusing on corresponding procedures starting with detection
(sensing) of a new device and subsequent mutual authenticating of the
device by and to the network a set of potential technologies are
identified and described to allow for analysis in terms of criteria
as reliable operation (working), scalability, ease of use and
convenience, security, and many more. Sensing is critical to
Hardware Based Authentication because sensing (together with
intelligent interpretation using possibly neural network models) will
allow the detection of the device playing a melody, blinking red and
blue, being pointed at, or somebody just touched and the like.
Furthermore, the method should be applicable to future generations of
network and of users, upcoming new applications and devices, assuming
that todays established standard procedures do not fulfill the
requirements sufficiently.
Hardware based authentication should leverage collaborative and
multi-band sensing technologies to enable sensing with much higher
precision and capacity using the state-of-art equipment. Also
equally important is the use of all artificial intelligence and
neural networks research results developed by the academia.
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3. State of the Academic Approaches to IoT Authentication
A detailed review on current topics in IoT Security, Device
Authentication and Access Control was provided in [Inayat]. The
following list of literature on sensor data and WiFi sensing for
securing and authenticating a user and a device shows the wide range
of approaches and interest in this topic [Rest21].
[Ma], [Wang], [Zhu], [Wang2], and [Qian] provide a holistic overview
on the evolution of Wi-Fi technology and on investigations in
opportunistic applications of Wi-Fi signals for gesture and motion
detection.
[Henning2] is investigating geospatial access control for IoT. There
are attribute, role and identity based, time based and geospatial
access control techniques. Real-world IoT access control policies
will be a combination of all three, leading to powerful access
control techniques to use in practice such as in university campus.
Such access control or authorization techniques will likely be used
in conjunction with Hardware Based Authentication.
Other notable literature includes [Al-Qaness] on the so-called
device-free CSI-based Wi-Fi sensing mechanism, [Pahlavan] using Wi-Fi
signals for gesture and motion detection as well as for
authentication and security, [Lui] distinguishing between Line-of-
Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in case of
obstacles appearing between the transmitter and the receiver [Guo]
studying HuAc (Human Activity Recognition) as a combination of WiFi-
based and Kinect-based activity recognition system, [FURQAN]
analyzing the wireless sensing and radio environment awareness
mechanisms, highlighting their vulnerabilities such as dependency of
sensing modes on external signals, and provides solutions for
mitigating them, e.g., the different threats to REM (radio
environment mapping) and its consequences in a vehicular
communication scenario.
[Ma2] has studied reliable SENS algorithm for human and animal
identification. The aim is to make it resilient to spoofing and
adverse channel conditions, i.e., presence of noise and interference
from other technologies.
[Restuccia] investigates data driven algorithms, neural networks,
especially convolutional neural network (CNN) or digital signal
processing (DSP) block to classify complex sensing phenomena. Also
[Liao] and [Liao2] proposed to enhance security of industrial
wireless sensor networks (IWSNs) by neural network based algorithms
for sensor nodes' authentication and implementations in IWSNs have
shown that an improved convolution preprocessing neural network
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(CNN)-based algorithm requires few computing resources and has
extremely low latency, thus enabling a lightweight multi-node PHY-
layer authentication.
Further research on these and similar issues can be found in [Tian],
[Bai], and [Axente].
4. IoT Authentication Protocols
Since IoT applications cover a broad range of domains from smart
cities, industry, and homes to personal (e.g., wearable) devices,
including security and privacy sensitive areas as e-health, and can
reach a huge number of entities the security requirements in terms of
preventing unauthorized access to data are very high. Therefore very
robust authentication mechanisms have to be applied. At the same
time depending on the specific scenario a trade-off between resources
as processing power and memory and security protocol complexity has
to be considered. Also a plethora of attack scenarios has to be in
focus as well as scalability of the considered implicit and explicit
hardware- and software-based authentication procedures. [RFC8576]
serves as a reference for details about IoT specific security
considerations including the area of authentication and documents
their specific security challenges, threat models, and possible
mitigations.
A more recent work surveys secure bootstrapping and onboarding
protocols [I-D.irtf-t2trg-secure-bootstrapping-00] developed by IETF
as well as other standards developing organizations such as IEEE,
FIDO alliance, Open Connectivity Foundation (OCF), Open Mobile
Alliance (OMA).
Lastly, the Open Authorization (OAuth) [RFC6749] protocol in the area
of authorization is a standard for access delegation. It extends
traditional client-server authentication by providing a third party
client with a token instead of allowing it to use the resource
owner's credentials to access protected resources while such token
resembles a different set of credentials than those of the resource
owner.
5. Hardware IoT Authentication Problem
Most of the state-of-art hardware identification techniques to
authenticate the user use finger prints a.k.a. touch id and facial
identification and they use detection by hardware i.e. touch,
accelerometer, and gyro sensors or cameras. They are based on
creating a signature, or the user's already stored password [Wang3].
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On the other hand to authenticate a device based on a set of
characteristic parameters which should be flexibly chosen by the
owner and subsequently made known to the authentication system will
require a certain level of processing and storage capacity either
within the local system components (e.g., the device itself and the
wireless point of attachment or access point) and/or within the
network (e.g., an edge cloud instance or a central data base). The
result of the detection process (e.g., radio wave analysis outcome in
terms of parameters as modulation scheme, number of carriers, and
fingerprinting) has to be compared with the required (correct)
parameter values which are safely stored within the network
components. On all levels of handling these data, i.e., storage,
processing, and transport via a communication network, the integrity
of the content has to be preserved. One should keep in mind, that
any unintended authentication request should be prevented to minimize
the risk of occasional attachment to networks and subsequent exposure
to attack to sensitive user data.
5.1. Architectural and Procedural Issues for Future IP-based IoT-
Authentication
Here we will discuss possible solutions on IP level and identify
benefits and potential gaps towards the requirements of next
generation IoT systems. On IP or network layer for IPv6 IPsec
protocol suite is mandatory and provides end-to-end security for
authentication procedures, ensuring confidentiality and integrity of
the transmitted data. Authentication for IoT may rely on a protocol
as 6LowPAN (Low-power Wireless Personal Area Network) which is
defined for optimizing the efficient routing of IPv6 packets for
resource constrained machine- type communication applications.
When compared to a fully certificate-based authentication, however, a
hardware-based AAA mechanism relying e.g., on WiFi sensing gesture
detection does not require the user to know any key, identifier, or
password for the device to be authenticated. A pre-defined type of
access to the device (e.g., physical, photographic or video
representation, unique description in terms of parameters, etc.)
shall be sufficient for authentication.
[RFC8995] on 'Bootstrapping Remote Secure Key Infrastructure' (BRSKI)
deals with authentication of devices, including sending
authorizations to the device as to what network they should join, and
how to authenticate that network by specifying automated
bootstrapping of an Autonomic Control Plane (ACP). Secure Key
Infrastructure (SKI) bootstrapping using manufacturer-installed X.509
certificates combined with a manufacturer's authorizing service, both
online and offline, is called the Bootstrapping Remote Secure Key
Infrastructure (BRSKI) protocol. Bootstrapping a new device can
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occur when using a routable address and a cloud service, only link-
local connectivity, or limited/disconnected networks and includes
support for deployment models with less stringent security
requirements. When the cryptographic identity of the new SKI is
successfully deployed to the device, completion of bootstrapping is
achieved. A locally issued certificate can be deployed to the device
via the established secure connection as well.
6. IANA Considerations
TBD.
7. Security Considerations
This document raises no new security concerns but tries to identify
how to increase security in future IoT by discussing the issues of
robust but easy to apply authentication mechanisms.
8. Acknowledgements
TBD.
9. References
9.1. Normative References
[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>.
9.2. Informative References
[Al-Qaness]
Al-Qaness, M.A.A., Abd Elaziz, M., Kim, S., Ewees, A.A.,
Abbasi, A.A., Alhaj, Y.A., and A. Hawbani, "Channel State
Information (CSI) from Pure Communication to Sense and
Track Human Motion: A Survey", Sensors 2019, 19(15),
3329 , July 2019.
[Axente] Axente, M.-S., Dobre, C., Ciobanu, R.-I., and R.
Purnichescu-Purtan, "Gait Recognition as an Authentication
Method for Mobile Devices", Sensors 2020, 20, 4110 , July
2020.
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[Bai] Bai, L., Zhu, L., Liu, J., Choi, J., and W. Zhang,
"Physical Layer Authentication in Wireless Communication
Networks: A Survey", Journal of Communications and
Information Networks Vol.5, No.3, September 2020.
[BFSFD] IEEE, "Institute of Electrical and Electronics Engineers,
IEEE P802.11 - TASK GROUP BF (WLAN SENSING) 11-21/0504r2
"Specification Framework for TGbf"", July 2021.
[BFUseCases]
IEEE, "Institute of Electrical and Electronics Engineers,
IEEE P802.11 - TASK GROUP BF (WLAN SENSING) 11-20/1712r2
"WiFi Sensing Use Cases"", January 2021.
[FURQAN] Furqan, H.M., Solaija, M.S.J., Tuerkmen, H., and H.
Arslan, "Wireless Communication, Sensing, and REM: A
Security Perspective", IEEE Open Journal of the
Communications Society Vol. 2 , January 2021.
[Guo] Guo, L., Wang, L., Liu, J., Zhou, W., and B. Lu, "HuAc:
Human Activity Recognition Using Crowdsourced WiFi Signals
and Skeleton Data", Hindawi Wireless Communications and
Mobile Computing, Volume 2018 , February 2021.
[Henning] Schulzrinne, H., "Do We Still Need Wi-Fi in the Era of 5G
(and 6G)?", February 2021.
[Henning2] Jan Janak, Luoyao Hao and Henning Schulzrinne, ., "How do
we program the Internet of Things at scale?", September
2021.
[I-D.irtf-t2trg-secure-bootstrapping-00]
Sethi, M., Sarikaya, B., and D. Garcia-Carrillo, "Secure
IoT Bootstrapping: A Survey", Work in Progress, Internet-
Draft, draft-irtf-t2trg-secure-bootstrapping-00, 7 April
2021, <https://www.ietf.org/archive/id/draft-irtf-t2trg-
secure-bootstrapping-00.txt>.
[IEEE802.11]
IEEE, "IEEE Std. 802.11-2016", December 2016,
<https://standards.ieee.org/findstds/
standard/802.11-2016.html>.
[IEEE802.1X]
IEEE, "Institute of Electrical and Electronics Engineers,
"802.1X - Port Based Network Access Control"", January
2020.
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[Inayat] Ali, I., Sabir, S., and Z. Ullah, "Internet of Things
Security, Device Authentication and Access Control: A
Review", International Journal of Computer Science and
Information Security (IJCSIS), Vol. 14, No. 8 , August
2016.
[Liao] Liao, R.-F., Wen, H., Wu, J., Pan, F., Xu, A., Jiang, Y.,
Xie, F., and M. Cao, "Deep-learning-based physical layer
authentication for industrial wireless sensor networks",
Sensors 2019, 19(11), 2440 , May 2019.
[Liao2] Liao, R.-F., Wen, H., Wen, H., Xie, F., Pan, F., Pan, F.,
and F. Xie, "Multiuser Physical Layer Authentication in
Internet of Things With Data Augmentation", IEEE Internet
of Things Journal, vol. 7, no. 3, pp. 2077-2088 , March
2020.
[Lui] Liu, J., Wang, L., Fang, J., Guo, L., Lu, B., and L. Shu,
"Multi-Target Intense Human Motion Analysis and Detection
Using Channel State Information", Sensors 2018, 18(10),
3379 , October 2018.
[Ma] Ma, Y., Arshad, et al, S., and , "Location-and Person-
Independent Activity Recognition with WiFi, Deep Neural
Networks, and Reinforcement Learning,", 2021.
[Ma2] Ma, Y. and G. Zhou, et al, "WiFi Sensing with Channel
State Information: A Survey,", ACM Computing Surveys
(CSUR), , vol. 52, no. 3, pp. 1-36, 2019.
[Pahlavan] Pahlavan, K. and P. Krishnamurthy, "Evolution and Impact
of Wi Fi Technology and Applications: A Historical
Perspective", Springer Science+Business Media, LLC, part
of Springer Nature 2020 , November 2020.
[Qian] Xian, K. and C. Wu, et al, "Widar: Decimeter-level Passive
Tracking via Velocity Monitoring with Commodity WiFi,",
Proc. of ACM MobiCom, , 2017.
[Rest21] Restuccia, F., "IEEE 802.11bf: Toward Ubiquitous Wi-Fi
Sensing", arXiv preprint arXiv:2103.14918 7 pages, March
2021.
[Restuccia]
Restuccia, F. and T. Melodia, "Deep Learning at the
Physical Layer: System Challenges and Applications to 5G
and Beyond,", IEEE Communications Magazine, , vol. 58,
no. 10, pp. 58-64, 2020.
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[RFC6749] Hardt, D., Ed., "The OAuth 2.0 Authorization Framework",
RFC 6749, DOI 10.17487/RFC6749, October 2012,
<https://www.rfc-editor.org/info/rfc6749>.
[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>.
[RFC8995] Pritikin, M., Richardson, M., Eckert, T., Behringer, M.,
and K. Watsen, "Bootstrapping Remote Secure Key
Infrastructure (BRSKI)", RFC 8995, DOI 10.17487/RFC8995,
May 2021, <https://www.rfc-editor.org/info/rfc8995>.
[Tian] Tian, Q., Lin, Y., Guo, X., Wang, J., AlFarraj, O., and A.
Tolba, "An Identity Authentication Method of a MIoT Device
Based on Radio Frequency (RF) Fingerprint Technology",
Sensors 2020, 20(4), 1213 , February 2020.
[Wang] Wang, X. and C. Yang, et al, "TensorBeat: Tensor
Decomposition for Monitoring Multiperson Breathing Beats
with Commodity WiFi,", ACM Transactions on Intelligent
Systems and Technology (TIST), , vol. 9, no. 1, pp. 1-27,
2017.
[Wang2] Wang, X. and C. Yang, et al, "PhaseBeat: Exploiting CSI
Phase Data for Vital Sign Monitoring with Commodity WiFi
Devices,", Proc. of IEEE ICDCS, , 2017.
[Wang3] Wang, H., Lymberopoulos, D., and J. Liu, "Sensor-Based
User Authentication", EWSN 2015, LNCS 8965, 168 , 2015.
[Zhu] Zhu, H. and F. Xiao, et al, "R-TTWD: Robust device-free
through-the-wall detection of moving human with WiFi,",
IEEE Journal on Selected Areas in Communications, ,
vol. 35, no. 5, pp. 1090-1103, 2017.
Authors' Addresses
Dirk von Hugo
Deutsche Telekom
Deutsche-Telekom-Allee 9
64295 Darmstadt
Germany
Email: Dirk.von-Hugo@telekom.de
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Behcet Sarikaya
Email: sarikaya@ieee.org
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