# Secret-Free Device Pairing in the mmWave Band

Ziqi Xu† Jingcheng Li† Yanjun Pan‡ Ming Li† Loukas Lazos† † Department of
Electrical and Computer Engineering, University of Arizona ‡ Department of
Computer Science and Computer Engineering, University of Arkansas Email: †
{zxu1969, jli2972, lim<EMAIL_ADDRESS>‡<EMAIL_ADDRESS>
###### Abstract.

Many Next Generation (NextG) applications feature devices that are capable of
communicating and sensing in the Millimeter-Wave (mmWave) bands. Trust
establishment is an important first step to bootstrap secure mmWave
communication links, which is challenging due to the lack of prior secrets and
the fact that traditional cryptographic authentication methods cannot bind
digital trust with physical properties. Previously, context-based device
pairing approaches were proposed to extract shared secrets from common
context, using various sensing modalities. However, they suffer from various
limitations in practicality and security.

In this work, we propose the first secret-free device pairing scheme in the
mmWave band that explores the unique physical-layer properties of mmWave
communications. Our basic idea is to let Alice and Bob derive common
randomness by sampling physical activity in the surrounding environment that
disturbs their wireless channel. They construct reliable fingerprints of the
activity by extracting event timing information from the channel state. We
further propose an uncoordinated path hopping mechanism to resolve the
challenges of beam alignment for activity sensing without prior trust. A key
novelty of our protocol is that it remains secure against both co-located
passive adversaries and active Man-in-the-Middle attacks, which is not
possible with existing context-based pairing approaches. We implement our
protocol in a 28GHz mmWave testbed, and experimentally evaluate its security
in realistic indoor environments. Results show that our protocol can
effectively thwart several different types of adversaries.

##  1\. Introduction

The increasing demand for higher data rates has pushed wireless technologies
to millimeter wave (mmWave) bands where bandwidth is abundant. The physics of
signal propagation at such short wavelengths offer some security advantages
compared to typical microwave communications due to the substantial signal
attenuation (which is proportional to the center frequency) and susceptibility
to blockage (Rappaport et al., 2017). For instance, eavesdropping becomes more
challenging as mmWave transmissions are highly-directional and do not travel
through physical barriers. Nevertheless, common security threats such as
eavesdropping, message injection, message modification, impersonation, and
Man-in-the-Middle (MitM) attacks are still feasible (Balakrishnan et al.,
2019b; Steinmetzer et al., 2018b) if wireless transmissions are left
unprotected.

The widely adopted approach for protecting wireless links is to incorporate
cryptographic solutions relying on asymmetric and symmetric cryptosystems
(Stallings et al., 2012). However, such solutions require extensive key
management for establishing and maintaining trust, which can pose scalability
challenges. Moreover, digital keys created from pseudo-random generators do
not bind the devices that use them with any surrounding physical property. For
instance, two devices that use a symmetric key to encrypt communications over
a public channel, cannot verify any other physical property such as location.
Extending trust to the physical world has become prevalent with the advent of
Next Generation (NextG) communications, where many applications seamlessly
integrate the cyber and physical worlds (e.g., industrial automation,
connected healthcare, connected autonomous vehicles).

A complementary approach to pre-loading pseudo-randomly generated secrets is
to generate secrets from common sources of randomness that exhibit high
spatial and temporal dynamics (Xu et al., 2021a). The main idea is that two
co-located devices can independently sample a high-entropy physical property
to derive a common key. The high spatial correlation of the samples at the
locations of the legitimate devices compared to the low correlation at the
adversary’s location offers the security advantage in the key generation
process. The so-called context-based key generation methods have notable
advantages such as reduced user efforts, perpetual key renewal, and high level
of security to remote adversaries. Commonly explored physical modalities
include light (Miettinen et al., 2014), sound (Xu et al., 2021b; Miettinen et
al., 2014; Schürmann and Sigg, 2011), wireless channel reciprocity (Liu et
al., 2013; Mathur et al., 2008), ambient RF signals (Mathur et al., 2011; Jana
et al., 2009), human operation (Li et al., 2020; Zhang et al., 2018), as well
as multi-modal approaches (Han et al., 2018; Farrukh et al., 2022).

###  1.1. Motivation and Challenges

Context-based secure pairing methods present numerous challenges. First, many
modalities (e.g., light, sound) suffer from low entropy, requiring long
sampling times to agree on common bits. They also require all devices to
possess a common sensing capability, which may not be available in IoT
applications. More importantly, they require physical access control to
guarantee security. The threat model assumes that the adversary has no
physical access to the environment where measurements take place. Moreover,
they are susceptible to active attacks where an adversary alters the physical
environment to make key generation predictable. RF modalities exhibit high
entropy in rich-multipath environments, which are common in sub-6 GHz
communications where the majority of prior works have been operating (Mathur
et al., 2011; Liu et al., 2013, 2012a). However, mmWave communications follow
geometric channel models (Akdeniz et al., 2014) with very few reflections
contributing to the channel uncertainty. Moreover, RF based methods have been
shown to be vulnerable to active attacks where an adversary, Mallory,
opportunistically injects signals to make bit extraction predictable (Eberz et
al., 2012).

Figure 1. Alice and Bob use mmWave probes to detect physical activity in the
line-of-sight path and agree on a common key. Mallory cannot sample the same
activity.

To counter the limitations of prior works and to transition device pairing
protocols to NextG networks, we explore the unique PHY-layer properties of
mmWave communications to develop a novel secret-free device pairing protocol.
Specifically, Alice and Bob independently derive common randomness by sampling
physical activity that disturbs their wireless channel in the presence of
Mallory. Wireless paths are interrupted by artificial or naturally-occurring
motion (e.g., human motion) within the environment at random times. This
interruption is sensed by measuring the amplitude of channel state information
(CSI) over the distinct paths that connect Alice and Bob. An example is shown
in Figure 1. The antennas of Alice and Bob are aligned over the line-of-sight
(LOS) path. The LoS path is interrupted by physical activity. Even if Mallory
is in the same environment, she observes a different path and therefore cannot
measure the same CSI variations. Mallory could observe the physical activity
using other modalities such as vision; however, it is exceedingly difficult to
estimate the impact of motion on the wireless channel at a very fine time
scale, thus leaving the pairing process secure.

There are several key technical challenges to design such a protocol that
senses ambient activity for device pairing in the mmWave band. First, due to
the small wavelength of mmWave signals, the CSI (and its amplitude) in the
mmWave band is very sensitive to small disturbances to the channel, and
channel reciprocity may not hold. Directly adopting measured CSI samples for
key agreement will introduce a high bit mismatch rate. Second, since mmWave
communication is highly directional, devices must have their beams aligned on
the direction(s) where activity exists to be able to sense it. However,
neither prior knowledge nor prior trust is available to support such beam
alignment in a secure manner. Third, the protocol must be resistant against a
wide range of threats, not only co-present passive adversaries but also active
attacks such as Man-in-the-Middle. This should be achieved without prior
trust.

###  1.2. Main Contributions

In this paper, we propose a novel context-based key pairing protocol for
devices that operate in the mmWave band, with merely in-band RF communication.
The basic protocol is called JellyBean, which exploits the pencil-like beams
used in mmWave communications to detect disturbances (channel blockage) to the
wireless channel due to physical motion from natural or artificial activity.
To solve the first challenge, we extract the timing of activities from the CSI
magnitude to generate a fingerprint of the ambient environment, which is used
as a common source of randomness between Alice and Bob. A passive adversary is
unable to infer the secret if observing the same activities from a different
vantage point due to the high directionality of mmWave transmissions.

Our basic pairing protocol is still vulnerable to a passive adversary that is
located in close proximity to either Alice or Bob and aligned in the sampled
path or an active adversary that launches beam-stealing attacks (Steinmetzer
et al., 2018b). Thus, we further introduce Jellybean+ which incorporates an
uncoordinated path-hopping (UPH) mechanism. UPH takes advantage of the
electronic antenna steering capabilities at mmWave to randomize the paths
sampled by Alice and Bob in a similar way as channel-hopping randomizes the
communication frequency. Without knowledge of which paths are sampled at any
point in time, a co-located adversary cannot construct fingerprints similar to
Alice and Bob. A notable difference from frequency hoping is that in UPH no
common hopping sequence is necessary for activity sensing.

We experimentally evaluate the performance and security of our protocols on a
28GHz mmWave testbed in typical indoor environments. Our experiments
demonstrate that the the bit mismatch rate between Mallory and Alice or Bob
far exceeds that of the legitimate devices, allowing for secure pairing in
reasonable time. We also show that the key derived from physical activities
satisfies the NIST randomness tests (Bassham III et al., 2010). To further
demonstrate the security of our protocols, we analyze both passive attacks
involving eavesdropping as well as recording of physical activities using
cameras.

The key novelty of Jellybean compared to the state-of-the-art context-based
pairing protocols is that it remains secure even if Mallory (a) Mallory is co-
located in the same environment and can observe the ongoing physical activity
and (b) launches an active attack. In fact, Jellybean+ remains secure even if
Mallory is at the same location as Alice or Bob.

##  2\. System and Threat Model

###  2.1. System Model

The system consists of an access point $A$ and a legitimate device $D$ located
in a typical indoor environment. Both devices operate in the mmWave band and
are equipped with electronically-steerable directional antennas. Their
antennas can be aligned using the Sector Level Sweep (SLS) protocol of IEEE
802.11ad (IEE, 2012), although any suitable beam alignment protocol can be
used. The devices aim at securely pairing and establish a common secret
$k_{A}=k_{D}=k$. The devices do not share any prior secrets.

|
---|---
(a) Passive attack | (b) Active beam-stealing attack
Figure 2. Threat model (a) Mallory passively observes the same environment as
$A$ and $D$, (b) Mallory launches an active beam stealing attack.

###  2.2. Threat Model

The goal of the adversary is to learn the common secret $k$ derived by $A$ and
$D$ during the pairing process. The adversary is equipped with an
electronically steerable antenna that can be pointed to any direction. Most
prior context-based pairing methods assume physical access control (Han et
al., 2018; Farrukh et al., 2022; Schürmann and Sigg, 2011; Miettinen et al.,
2014). The adversary is assumed to be unable to observe the environment
sampled by $A$ and $D.$ For instance, the legitimate devices and the adversary
are separated by a physical barrier such as a wall that makes physical
properties such as light, sound, and human activity unobservable. Our threat
model breaks this rather artificial assumption and allows the adversary to be
in the same environment as $A$ and $D$, as shown in Figure 2(a).

The adversary has full knowledge of the context-based pairing protocol
executed by the legitimate devices. However, she does not control the physical
activities taking place in the environment as this is easily visually
detected. Mallory could be either passively trying to derive the secret $k$ or
launch an active attack.

Passive attacks: A passive Mallory can steer her antenna to $A$ and $D$
respectively, to eavesdrop on any messages exchanged between the two parties.
Moreover, Mallory can steer her antenna to any other desired direction
including the location where physical activities may take place. Mallory can
position herself at a location of her own choosing. This assumption is fairly
strong, as the presence of Mallory in private spaces can be easily detected.
However, it may hold for public spaces. Moreover, it provides the worst-case
analysis on the protocol security. Finally, Mallory may have access to a
separate modality such as vision (camera) that allows her to observe the
physical activities occurring in the environment. The resolution is limited by
the modality used (e.g., frame rate).

Active Attacks. Mallory can inject her own signals to compromise the device
pairing process. We assume that the purpose of signal injection is the
compromise of the secret derived between $A$ and $D$ (Pan et al., 2021) and
not a denial-of-service (DoS) attack. The latter is fairly easy to launch
against device pairing protocols using interference attacks such as selective-
jamming. Moreover, Mallory can launch a beam-stealing attack that allows the
implementation of a MitM adversary in directional mmWave communications
(Steinmetzer et al., 2018b). The main idea of a beam stealing is shown in
Figure 2(b). Mallory manipulates the beam alignment process between $A$ and
$D$ by forging sector sweep frames with high RSS. $A$ and $D$ can only
communicate through Mallory as their antennas are aligned to $M$. Mallory can
then launch a MitM attack against the pairing process between $A$ and $D.$ We
note that although several methods have been proposed to add authentication to
the beam alignment process (Steinmetzer et al., 2018a; Balakrishnan et al.,
2019a; Wang et al., 2021a, 2020), they require prior trust to be established,
or training for the purpose of fingerprinting and thus are not applicable in
the absence of trust.

##  3\. Protocol Design

We design a context-based secure pairing protocol suitable for mmWave
communications. Security is drawn from the random physical motions in the
environment (e.g., human activity). The motions could occur naturally in high-
traffic scenarios, or could be artificially induced by the user. We emphasize
that although physical activity has been used as a source of randomness in
prior works (Li et al., 2020; Zhang et al., 2018), it affects mmWave
transmissions in a different way than microwave transmissions. In microwave
frequencies, motion is observed primarily via multipath and scattering whereas
in mmWave, it is primarily observed as blockage, which is path-specific. This
path specificity provides an opportunity to build stronger security compared
to microwave-based methods, that are not to be vulnerable to non-LoS sensing
(Zhu et al., 2020).

The legitimate devices $A$ and $D$ align their antennas over a feasible
communication path using a beam alignment protocol such as the sector level
sweep process in 802.11ad (IEE, 2012). The random motions in the environment
disrupt the $A-D$ channel in an unpredictable manner in terms of timing and
duration. These disruptions are unique to the path between $A-D$ due to the
high antenna directionality of mmWave communications and prevent Mallory from
recording similar channel disruptions by observing the same physical activity
from a different vantage point.

Some vantage points may allow the observation of similar disruptions as the
$A-D$ channel. For instance, when $M$ is aligned with the $A-D$ LoS channel.
To secure against these special locations, we add diversity in the paths
sampled for activity. Rather than relying on a single path, $A$ and $D$
randomly hop to other available paths (e.g., through a reflection) in an
uncoordinated fashion, thus randomizing the direction over which activity is
observed. Even if the adversary can measure channel activity on all paths
(e.g., by fast switching or deploying multiple antennas), she does not know
the antenna directions for $A$ and $D$, thus being unable to infer which
activity is used in key generation. Our uncoordinated path hopping method
resembles frequency hopping to avoid eavesdropping and jamming (Strasser et
al., 2008), but it occurs without a commonly shared hopping sequence and in
the space domain.

###  3.1. Fingerprinting Physical Activity

The core security primitively used in our secure key extraction process is the
unpredictability of how physical activity impacts a mmWave path at a fine time
scale. To fingerprint physical activity, the two legitimate devices $A$ and
$D$ align their antennas over a feasible communication path (e.g., the LoS
path), as shown in Figure 2(a). The two devices exchange probe messages to
measure the CSI amplitude independently. These measurements are processed to
extract the start and end time of the physical activity, which is used to
independently derive common bits. We emphasize, that contrary to previous CSI-
based methods that exploit channel reciprocity (Mathur et al., 2008), we do
not use the CSI amplitude directly for key extraction. This is because the CSI
has low entropy in mmWave frequencies due to its geometric nature when the
path is not disrupted and channel reciprocity breaks under motion on the
sensed path.

The main challenge lies in independently processing the CSI samples to achieve
a high bit agreement rate while maintaining the entropy provided by the
physical motion. We meet this challenge by applying the processing steps shown
in Figure 3. First, the CSI samples are denoised and smoothed to filter
inherent channel fluctuations introduced by the fine nature of CSI measurement
in mmWave. Then, we apply threshold detection to convert CSI sample sequences
to a bitstream indicating the presence or absence of activity. Finally, we
encode the bitstream to increase the entropy of the derived common secret. We
describe each step in detail for device $A$. The same process is applied at
$D$.

Figure 3. Workflow of the activity fingerprinting process.

1\. Channel sounding. To sense physical activity, devices $A$ and $D$ exchange
probes used for channel sounding (Rappaport, 2010). The exchange of two probes
completes one probing round. At the end of $N$ rounds, the CSI samples are
organized into sets $CSI_{A}$ and $CSI_{D}$, respectively. We focus on set
$CSI_{A}$ and drop the $A$ subscript for ease of notation. The CSI set is
denoted by $CSI=\\{c_{1},c_{2},\ldots c_{K}\\}$ and each $c_{i}$ is the set of
samples collected per subcarrier. The probes are relatively short, containing
a preamble and the unauthenticated device ID.

2\. Sample denoising and smoothing. To reduce the noise in CSI measurements,
the wavelet transform is applied on each $c_{i}$ (Yu et al., 2018). As
channels may be affected differently at various subcarrier frequencies, we
compute the cross-correlation between the CSI amplitude samples at each
subcarrier and the center subcarrier $K/2$. For two subcarriers $i$ and $j$
with CSI sample sequences $c_{i}$ and $c_{j}$ the cross-correlation is given
by

(1) $\displaystyle\rho(i,j)=\frac{1}{\alpha N-1}\sum\limits_{k=1}^{\alpha
N}(\frac{c_{i}(k)-\mu_{c_{i}}}{\sigma_{c_{i}}})(\frac{c_{j}(k)-\mu_{c_{i}}}{\sigma_{c_{j}}})$

where $\mu_{c_{i}}$ and $\sigma_{c_{i}}$ are the mean and variance of set
$c_{i}$ and $\alpha$ is the number of CSI samples extracted per probe and per
subcarrier. The sample sets $c_{1},c_{2},\ldots c_{K}$ are ranked according to
their cross-correlation with the $K/2^{th}$ subcarrier and only the top half
is retained. The new set of samples is denoted by $CSI=\\{c_{1},c_{2},\ldots
c_{K/2}\\}$ where the set indices correspond to the ranked list of
subcarriers.

To further alleviate the high sensitivity of the CSI amplitude measurements in
the mmWave channel, we apply a $W_{m}$-point moving variance operation to the
CSI amplitude along each subcarrier set $c_{i}$. Given a set of samples
$c_{i}=\\{c_{i}(1),c_{i}(2),\cdots\,c_{i}((\alpha N)\\}$, a $W_{m}$-point
moving variance for a sample $c_{i}(j)$ is computed by

$MV(c_{i}(j))=\frac{1}{W_{m}-1}\sum\limits_{c(m)\in X_{j}}(c(m)-\mu_{j})^{2},$

where
$X_{j}=\\{c_{i}(j-\lfloor\frac{W_{m}-1}{2}\rfloor),\cdots,c_{i}(j),\cdots,c_{i}(j+\lfloor\frac{W_{m}-1}{2}\rfloor)\\}$
and $\mu_{j}$ denotes the mean of $X_{j}$. We opt to use CSI amplitude
variance to capture changes in the CSI amplitude because they are indicative
of channel blockage. The moving variance operator is applied to smooth the
rapid fluctuations. In the next step, the $W_{m}$-point moving variance is
averaged over the $K/2$ highest correlated subcarriers to produce a single
time series of CSI amplitude, denoted as
$\mathcal{T}=\\{c(1),c(2),\ldots,c(\alpha N)\\}.$ Finally, to cope with the
lack of perfect channel reciprocity and the sensitivity of the mmWave channel
to small geometry changes (Zhang et al., 2015), an averaging window of $W_{s}$
is applied across $\mathcal{T}$ to downsample it by a factor of $W_{s}$. The
final CSI sequence is denoted by $\Gamma$ and has a size of $\lfloor\alpha
N/W_{s}\rfloor$ samples.

3\. Detecting Activity. Physical activity on a time series $\Gamma$ is
inferred by using threshold-based detection (Rousseeuw and Croux, 1993). The
process generates a bit sequence $B$ by comparing every sample in $\Gamma$
with a detection threshold $\tau$:

$\displaystyle b(i)=\left\\{\begin{aligned} 1,&\quad\gamma(i)\geq\tau,\\\
0,&\quad\text{otherwise}.\\\ \end{aligned}\right.$

The threshold $\tau$ is defined as the average over the $\ell$ lowest CSI
amplitude samples in $\Gamma$, where $\ell$ corresponds to a short time
duration (e.g., one second). Given the time scale of physical motion, this
duration will correspond to a period of inactivity.

4\. Encoding. Due to the high sampling rate relative to the speed of any
physical motion, bit sequence $B$ consists of many consecutive zero bits
(inactivity) followed by consecutive one bits (activity), thus having
relatively low entropy. The random part of the physical motion sensed by the
probes lies in the start time and duration of the channel disruption. To
encode that information alone, we apply run-length encoding (RLE) to transform
sequence $B$ into blocks. In RLE, each block of consecutive bits (zeros or
ones) is encoded to a counter representing the block length (duration),
followed by the block’s bit value (activity or inactivity). An example of the
application of RLE to an example $B$ is given below (the underlined bits
represent the block counter).

$B$ = | 000000 | 1111 | 000 | 111 | 00000 | 1111
---|---|---|---|---|---|---
RLE[$B$] = |  60 |  41 |  30 |  31 |  50 |  41
= | 1100 | 1001 | 0110 | 0111 | 1010 | 1001

Additionally, we apply Gray encoding to reduce the mismatch between the
sequences generated by $A$ and $D$ due to slight mismatches in the block
lengths. The number of bits used in Gray code is determined by the longest
consecutive bits counted by RLE. In the example, the longest consecutive bit
is 6. So we use a 3-bit Gray code in each block. In Gray encoding, two
successive counters differ by exactly one bit. The Gray-encoded sequence for
the example above is represented by $S$:

$S$ = | 1010 | 1101 | 0100 | 0101 | 1110 | 1101
---|---|---|---|---|---|---

The final step is to truncate each block to the same length, so that events
contribute equally to the final sequence. We achieve this by retaining $N$
least significant bits (LSBs) from each gray-coded block. We retain LSBs
because they are harder to predict and thus increase the entropy of the
sequence. The final sequence upon the application of truncation of $N=3$ in
the running example is

$F=010~{}101~{}100~{}101~{}110~{}101.$

Sequence $F$ corresponds to the final bit sequence that will be used as an
activity fingerprint in the agreement to a common key. At the end of the
independent CSI measurement processing, each device $A$ and $D$ has computed
$F_{A}$ and $F_{D}$, respectively.

###  3.2. The Jellybean Pairing Protocol

We integrate the activity fingerprinting process into a secure device pairing
protocol called Jellybean. The Jellybean protocol allows $A$ and $D$ establish
a common secret $k$ and prevents Mallory from learning $k$. Moreover, it
provides authentication through presence. If $M$ were to execute the same
protocol with $A$ (either asynchronously or in parallel), she would establish
a different secret. The protocol consists of the activity fingerprinting (AF)
phase followed by the key agreement phase, which is a standard fuzzy-
commitment based key agreement (Juels and Wattenberg, 1999). The two phases
are illustrated in Figure 4 and described below.

Initialization

  1. (1)

$A$ and $D$ align their antennas using a beam alignment protocol (IEE, 2012).

  2. (2)

$A$ and $D$ agree on parameters $W_{m},W_{s},N$, and the Reed-Solomon code
$RS$. The parameters could also be preset.

Activity Fingerprinting ($AF$)

  1. (5)

$D$ and $A$ execute probing rounds and collect $CSI_{D}$ and $CSI_{A}$,
respectively.

  2. (6)

$D$ and $A$ generate fingerprints $F_{D}$ and $F_{A}$, respectively, using the
activity fingerprinting process described in Section 3.1.

Key Agreement

  1. (7)

$A$ generates a random secret key $k$ and creates commitment
$\delta_{A}=F_{A}\ominus ENC_{RS}(k)$, where $\ominus$ denotes subtraction in
a Galois Field of $\mathbb{F}_{q}^{n}$, and $ENC_{RS}(\cdot)$ is an RS
encoder. $A$ appends $h(k)$ to $\delta_{A}$ and sends the resulting value to
$D$, where $h(\cdot)$ is a secure hash function.

  2. (8)

Upon receiving the commitment $\delta_{A}$ and $h(k)$, $D$ opens the
commitment by decoding secret $k^{\prime}=DEC_{RS}(F_{D}\ominus\delta_{A})$
using its independently derived fingerprint $F_{D}$, where $DEC_{RS}(\cdot)$
is the RS decoding function. $D$ then verifies
$h(k^{\prime})\overset{?}{=}h(k)$. If the verification succeeds, $D$ accepts
the secret. Otherwise, it aborts the pairing process.

The key agreement phase is an application of a fuzzy commitment scheme (Juels
and Wattenberg, 1999) that has been widely used in context pairing (Farrukh et
al., 2022; Han et al., 2018; Miettinen et al., 2014). It can reconcile two
similar sequences that differ up to the error correction capability of the
selected error correcting code. To conceal the secret $k$, $A$ computes
$\delta_{A}=F_{A}\ominus ENC_{RS}(k)$ which reveals no information about $k$
($k$ is pseudo-random and $F_{A}$ is unknown to $M$. Upon receiving the
commitment $\delta_{A}$, $D$ can open the commitment by applying
$k^{\prime}=DEC_{RS}(F_{D}\ominus\delta_{A})$ and recover $k^{\prime}$. The
value $k^{\prime}$ is equal to $k$ only if the Hamming distance between
$F_{D}$ and $F_{A}$ is below the error-correcting capability of the RS code.
Finally, the integrity of $k^{\prime}$ is checked using the attached hash
$h(k).$

Hub $A$ |  | Device $D$
---|---|---
Parameters: |  | Parameters:
($W_{m},W_{s},N,RS$) |  | ($W_{m},W_{s},N,RS$)
Activity Fingerprinting (AF):
Collect $CSI_{A}$ |  | Collect $CSI_{D}$
Compute, |  | Compute,
$F_{A}\leftarrow AF(CSI_{A})$ |  | $F_{D}\leftarrow AF(CSI_{D})$
Key Agreement:
Generate a random $k$
Compute $h(k)$
$\delta_{A}\leftarrow F_{A}\ominus ENC_{RS}(k)$
$m_{A}(3)\leftarrow\delta_{A}||h(k)$ |  |
| $\xrightarrow{~{}m_{A}(1)~{}}$ | Decode,
|  | $k^{\prime}\leftarrow DEC_{RS}(F_{D}\ominus\delta_{A})$
|  | Verify $h(k^{\prime})\overset{?}{=}h(k)$

Figure 4. The Jellybean pairing protocol.

##  4\. Parameter Selection and Security Evaluation

In this section, we show how to select the parameters of Jellybean to achieve
correctness (secure pairing of a legitimate device) and soundness (leakage of
$k$ to Mallory). We further experimentally evaluate Jellybean in terms of
performance and security.

###  4.1. Evaluation Metrics

To evaluate the security and performance of Jellybean, we employ the following
metrics that are commonly used for context-based pairing methods.

Bit Mismatch Rate (BMR): the ratio of the Hamming distance between two
fingerprints over the fingerprint length. The BMR between $F_{A}$ and $F_{D}$
is used as a metric for protocol correctness, whereas the BMR between $F_{A}$
and $F_{M}$ is used as a metric for protocol soundness.

Secret Bit Rate (SBR): the average number of bits extracted per any activity
interrupting the $A$-$D$ channel. Here, we define the SBR per activity as
opposed to per unit of time, because the activity rate could vary depending on
the environment.

Approximate Entropy (ApEn): A measure of the randomness and unpredictability
of a time series. It is preferred over entropy because it provides a more
accurate measure of randomness for finite-length series (Pincus, 1991).

The Jellybean protocol parameters involve tradeoffs between security and
performance. For example, the RS code needs to be carefully selected to allow
legitimate devices to pair but prevent Mallory from inferring the common key.
We study these tradeoffs by co-evaluating correctness and soundness.

###  4.2. Experiment Setup

---
(a) device connection of each link
(b) Room A, 438 sq.ft
(c) Room B, 852 sq.ft
Figure 5. Experiment setup and topologies.

Our experimental setup consisted of USRPs and connected to the TMYTEK mmWave
platform (TMY, [n. d.]). Specifically, we implemented the baseband
functionality of $A$, $D$ and $M$ on USRP N200s. Each USRP was connected to a
TMYTEK up-down (UD) converter that upscaled the signal from the sub-6GHz band
to a center frequency of 28GHz with 160MHz bandwidth. Transmission took place
via a directional BBox lite patch antenna with 16 elements. The antenna
supports electronically steerable sectors with half-power beamwidth of
$30^{\circ}$, covering the plane with 12 non-overlapping sectors. The maximum
transmit power of the USRPs was 30dBm. On the receiver side, the captured 28
GHz signal was down-converted and processed by the USRP. The platform
configuration is shown in Figure 5(a).

The exchanged probes consisted of 1,460 bytes and were transmitted using a
5MHz bandwidth over 52 subcarriers. The sampling rate for the USRPs was set to
5 MHz enabling the collection of 3,100 CSI samples for each subcarrier per
second. The probing process for CSI measurements requires rapid and continuous
switching between signal transmission and reception. Due to the half-duplex
capabilities of USRPs N200 and their slow switching capabilities, we performed
the probing between $A$ and $D$ using two horizontally-aligned links, as shown
in Figures 5(b) and 5(c), where each mmWave communication link consists of the
setting in Figure 5(a). Figures 5(b) and 5(c) also show the layout of the two
office rooms where we conducted our experiments.

We studied both artificial and natural motions within the environment. To
create artificial motions in the $A$-$D$ channel, we waived a hand vertically
across one of the antennas to disrupt the channel. The hand was waived at
natural speeds and also horizontally to disrupt any paths beyond the LoS. The
artificial motion reflects a scenario where a user is instructed to waive his
hand across the device that he intends to pair and provides a usable and quick
way for device pairing.

For natural motions, the office occupants walking through the space disrupting
different paths at different times. The natural motion reflects a scenario
where the natural movement in the space is exploited to pair device as well as
continuously renew trust.

###  4.3. Parameter Selection

(a) | (b)
---|---
Figure 6.  (a) BMR as a function of $W_{m}$ and (b) ApEn as a function of
$W_{m}$. Figure 7. Performance evaluation of Jellybean with two types of
activities in room B.

The Jellybean protocol is controlled by three parameters: the moving variance
window $W_{m}$, the averaging window $W_{s}$, the number of retained LSBs $N$
in each RLE block, and the RS code for key agreement. In this section, we show
how to select these parameters in practice and then evaluate the Jellybean
protocol under different attack scenarios. The CSI samples used for parameter
selection are collected in 90 second intervals, during which the LoS path of
$A$-$D$ channel in room A is interrupted by the occupant with artificial and
daily activities. The approximate entropy of the fingerprint is calculated
using the NIST randomness test suite (Bassham III et al., 2010). Moreover, we
obtained the $p$-value from the NIST tests which indicates if a sequence is
sufficiently random to be used for security purposes. The $p$-value must
exceed 0.01. For all the ApEn plots, we utilize empty markers to represent
results with a $p$-value lower than 0.01, which indicates a failure in the
NIST randomness test. Conversely, filled markers indicate a successful test.

Selecting $W_{m}$. The moving variance window $W_{m}$ alleviates the high
sensitivity of the CSI amplitude measurements. It needs to be long enough to
smooth the rapid fluctuations, but should not eliminate the CSI randomness.
Figure 6 presents the BMR and ApEn as a function of $W_{m}$ for both
artificial and natural daily activities. To simplify the analysis, $W_{m}$ is
converted from the number of CSI samples to seconds based on the sampling
rate. We choose the smallest $W_{m}$ value that results in a BMR of less than
0.2 and an ApEn with $p$-values exceeding 0.01. As a result, we set $W_{m}$ to
0.1s for artificial activities and 0.25s for daily activities. This selection
balances the functionality and security of the Jellybean protocol while
ensuring a sufficiently random fingerprint is generated.

(a) | (b)
---|---
Figure 8.  (a) BMR as a function of $W_{s}$, and (b) ApEn as a function of
$W_{s}$.

Selecting $W_{s}$. The averaging window $W_{s}$ provides an additional level
of smoothing to account for imperfect channel reciprocity and the sensitivity
of the mmWave channel to small changes in geometry. It is also of great
significance to avoid prolonging $W_{s}$ that can result in a predictable
fingerprint. In Figure 8, we show the BMR and ApEn as a function of $W_{s}$.
We observe that the BMR monotonically decreases as $W_{s}$ increases. However,
compared to $W_{m}$, the Jellybean protocol is more sensitive to changes in
$W_{s}$, and the generated fingerprint can easily become predictable, failing
the NIST randomness test for many $W_{s}$ values. We choose the smallest
$W_{s}$ value that yields a BMR below 0.2 and an ApEn with a $p$-value greater
than 0.01, indicating a usable fingerprint. Consequently, we let $W_{s}=300$
and $W_{s}=600$ for artificial and daily activities, respectively.

(a) | (b)
---|---
Figure 9. (a) BMR as a function of $N$, and (b) ApEn as a function of $N$.

Selecting $N$. The number of LSBs maintained from each CSI block control the
contribution of each event (activity or inactivity) to the final bit sequence
used as fingerprint. The selection of $N$ also impacts the entropy of the
final bit sequence as a longer $N$ could lead to more predictable sequences.
Figure 9 shows how BMR and ApEn vary with $N$. As expected, a smaller $N$
leads to a higher BMR but also a higher ApEn. To best balance the
functionality and security of the Jellybean protocol, we fix $N=5$ for both
artificial and daily activities.

Selecting the RS code. The final parameter to be selected is the RS code used
in the key agreement phase. The code’s error correcting capability must be
high enough to allow for the observed BMR, but low enough so that $k$ is not
leaked to Mallory. Based on the previously selected parameters and the
obtained BMR values, we considered an RS code that tolerated a 20% mismatch
between the two fingerprints. This is inline with codes used in previous
context-based methods.

###  4.4. Performance Evaluation of Jellybean

In this section, we evaluate the performance of Jellybean protocol using the
parameters set above. To test the protocol robustness to environmental
changes, we conducted additional experiments in room B. Room B has a larger
space and a more complicated RF environment compared to room A. The layout of
Room B is shown in Figure 5(c). We conducted additional experiments in room B,
shown in Figure 5(c), to illustrate varying $A$-$D$’s distance impact on
evaluation metrics BMR, SBR, and ApEn. We varied the distance between $A$ and
$D$ from 1m to 8m. For each distance, we collected CSI samples for three
minutes while performing artificial and natural daily activities. Figure 7
shows the BMR, SBR, and ApEn at different distances. We observe a sound trend
of the three metrics, which indicates that the $AF$ algorithm can develop
activity-based fingerprints with a low BMR, an acceptable SBR, and NIST-
approved ApEn.

###  4.5. Security Analysis for Jellybean

In this section,we analyze the security of our Jellybean protocol against
different types of adversaries.

|
---|---
(a) an eavesdropping $M$ | (b) a keylogging $M$
(c) a nearby eavesdropper $M$
Figure 10. Experiment settings for evaluating Jellybean with different types of $M$. results in room A | results in room B
---|---
(a) $M$ with an intersection angle $\Phi=0^{\circ}$
(b) $M$ with an intersection angle $\Phi=20^{\circ}$
(c) BMR of $F_{A}$ and $F_{M}$ as a function of $\Phi$, $F_{M}$ after the dash
line
is generated with random guess due to the trivial CSI samples
received at $M$.
Figure 11. Evaluation of Jellybean for an eavesdropping $M$ in room A (the
left column) and room B (the right column).
---
(a) artificial activity
(b) daily activity
Figure 12. Security evaluation of the Jellybean for a keylogging $M$ with two
types of activities in experimental room A.
---
(a) path discovery
(b) uncoordinated path hopping
Figure 13. An example of the path discovery phase and path hopping phase in
UPH. The numbers indicate the chosen sectors at each device. If $A$ and $D$
simultaneously switch to a usable sector pair (2-4 and 1-5 in the example),
the probing round is successful and activities are measured.

1) Eavesdropping Adversary: We first consider an eavesdropper $M$ is
positioned at the same distance from $A$ as $D$. This positioning is intended
to ensure a similar channel loss for $A$-$M$ channel as for $A$-$D$ channel,
thereby enabling $M$ to actively adjust its angle from the $A$-$D$ channel to
identify the best eavesdropping opportunities. Due to the hardware limitation,
we only used four USRPs N200 to implement $A$, $D$, and $M$, as shown in
Figure 10(a). We maintained the distance for $A$-$D$ and $A$-$M$ links as 4m
and collected 2-minute CSI samples with different angles $\Phi$ between
$A$-$D$ and $A$-$M$ channels. To ensure fairness, activities were conducted
near hub $A$, allowing activities observations for both $D$ and $M$.

Due to the directionality of the directional beam produced by the phased array
beamformer, the probing signal emitted by $A$ gets lost at $M$ when $\Phi$
exceeds a certain value. To compensate the sample loss and compare the
fingerprint of $M$ with that of $A$, we fill in the corresponding missing bits
in $F_{M}$ to ensure that $F_{A}$ and $F_{M}$ have the same length. We
employed three strategies for this purpose: 1) random guess that involves
randomly filling in a bit to replace the missing one in $F_{M}$, 2) bit
reusing that reuses a segment of previously extracted bits from $F_{M}$, and
3) no filling that leaves the missing bit blank. Figure 11(c) shows the BMR
between $F_{A}$ and $F_{M}$ as a function of $\Phi$ for these three
strategies. We observed a high BMR between $F_{A}$ and $F_{M}$, except when
$\Phi=0^{\circ}$. Figures 11(a) and (b) further present the moving variance of
the 45-second CSI samples collected by $D$ and $M$, respectively, for
$\Phi=0^{\circ}$ and $\Phi=20^{\circ}$. When $\Phi=20^{\circ}$, there is a
significant difference in observations between $D$ and $M$, resulting in a
failed key agreement process at $M$. However, when $M$ perfectly aligns with
the $A$-$D$ link at $\Phi=0^{\circ}$, she can successfully retrieve the agreed
key between $A$ and $D$ based on her own observations, which exhibit a
relatively low BMR of 0.2. Such a strong eavesdropper is referred to as a
nearby eavesdropper and we introduce an upgraded protocol called Jellybean+ in
Sec. 5 to thwart it. In summary, the basic Jellybean protocol is secure
against a co-located eavesdropper outside the close proximity to $A$ or $D$.

2) Keylogging Adversary: A keylogging adversary uses vision instead of RF
devices for activity fingerprinting. $M$ monitors all the activities taking
place in the shared area between $A$ and $D$ with a camera, as shown in Figure
10(c). She determines the start and ending time for each activity by examining
all the video recordings frame by frame and fingerprints the activity by
filling bits 1s in the duration of each activity and 0s for the rest.

We let the keylogging adversary record the activities with a cell phone and we
evaluate the performance of it by comparing the start and end time of the
activities extracted by $M$ and $D$. Figure 12 shows the activity detection
results for $D$ and $M$ with one artificial and daily activity, respectively.
We can see that due to the denoising and smoothing process in the Jellybean
protocol, $D$ detects the duration of one artificial activity in a range of
0.08-0.1 seconds, whereas $M$ records a longer duration spanning 1-3 seconds
for the same activity. Similarly, for daily activities, $M$ detects the
activity with a significantly longer duration compared to $D$. This
substantial discrepancy in activity fingerprinting between $M$ and $D$ makes
it impossible for $M$ to agree on the same key as established between $A$ and
$D$.

3) Beam-stealing Adversary. $M$ deceives $A$ and $D$ by manipulating the beam
alignment process. $M$ forges sector sweep frames with high received signal
strength (RSS), leading $A$ and $D$ to align their beams with $M$ instead of
each other (Steinmetzer et al., 2018b). This beam-stealing attack allows $M$
to launch a MitM attack against the pairing protocol.

The basic Jellybean protocol is vulnerable to this attack due to the lack of
authentication in the beam alignment process. With the amended Jellybean+
protocol introduced in Sec. 5, we remove the assumption of beam alignment and
instead employ an uncoordinated path-hopping (UPH) mechanism. This approach
takes advantage of multiple paths and utilizes the electronic antenna steering
capabilities of mmWave technology to randomize the activity sensing area. By
adopting Jellybean+, we not only defend against the strong nearby
eavesdropper, but we also mitigate the threat posed by an active beam-stealing
adversary.

##  5\. Uncoordinated Path Hopping

Based on the security analysis, the activity fingerprinting process is
vulnerable to two attacks. First, a passive adversary that is aligned with the
path observed by $A$ and $D$ will extract a similar fingerprint to $F_{A}$ and
$F_{D}$ (with a BMR below 0.2 as seen in Figure 11(c)), thus also being able
to obtain $k$ from the commitment value $\delta_{A}.$ Second, an active
adversary launching a beam-stealing attack can become a MitM between $A$ and
$D$. In this scenario, $M$ can independently establish keys with $A$ and $D$
using the activities in the $A$-$M$ and the $M-D$ paths, respectively.

To prevent these attacks, we propose the Jellybean+ protocol that exploits the
potential availability of multiple communication paths between $A$ and $D$
(e.g., LoS path and one reflection path). The main idea is to “hop” (steer)
the antennas between paths and merge activities from each path (direction)
into the same fingerprint. Without knowing which path to monitor at any given
time, Mallory cannot extract the same fingerprint, even if she can observe all
paths. Likewise, in a beam-stealing attack scenario, the activities toward the
direction of Mallory contribute to the fingerprint only partially, since
activities on other paths are also present. Evidently, path hopping is similar
to frequency hopping but applied in the space domain as opposed to the
frequency domain.

There are two main challenges for applying path hopping in our context, due to
the lack of initial trust. First, how do $A$ and $D$ discover viable
communication paths (paths where probe decoding is possible) and second, how
do $A$ and $D$ securely share a common path hopping sequence. We address both
challenges by developing an uncoordinated path-hopping mechanism (UPH), where
$A$ and $D$ hop independently between the available paths. The UPH mechanism
has two phases; the path discovery phase and the path hopping phase.

1\. Path discovery. To discover the available paths between $A$ and $D,$ we
propose a new beam alignment process that keeps track all transmitter-receiver
antenna sector combinations that yield an RSS that exceeds the receiver’s
sensitivity by a margin $\epsilon.$ The path discovery process is demonstrated
in Figure 13(a). Both $A$ and $D$ sweep the plane but with different periods.
$A$ sweeps slowly whereas $D$ sweeps fast. The protocol proceeds in rounds.
During a round, $A$ fixes its antenna at one sector whereas $D$ sweeps through
all sectors in random order. Consider $A$ pointing to $S_{A}$ whereas $D$ is
pointing to $S_{D}$. Device $D$ sends a probe (sector sweep frame) to $A$. If
the probe is correctly decoded and the RSS exceeds the receiver sensitivity by
the margin $\epsilon$, $A$ replies with each own probe from sector $S_{A}.$ If
$A$’s probe is also decodable, $D$ records sector $S_{D}$ as viable and
responds to $A$ with an ACK. Upon reception of the ACK, $A$ also records
$S_{A}$ as viable. This process is repeated for every sector of $D,$ while $A$
remains on the same sector. If multiple sectors of $D$ can be received by $A$,
$D$ chooses the strongest one (highest RSS). In the next round, $A$ randomly
switches to another sector and the probing process is repeated.

At the end of the path discovery process, each party learns the set of viable
sectors that can achieve communication with the other party. Note that each
party only knows its own viable sectors. In the example of Figure 13(a), we
show several rounds of path discovery. In round 1, $A$ steers to sector
$S_{A}=1$ while $D$ sweeps through all eight sectors. Successful communication
takes place in sector pairs $1-5$ and $1-6$. $A$ records sector 1 as a viable
sector and $D$ records sectors 5 and 6 as viable sectors. Because the RSS on
sector 5 is higher, only sector 5 is recorded. The process is repeated on
future rounds. At the completion of 8 rounds, two paths yield viable
directions, namely 1-5 (LoS) and 2-4 (reflection).

2\. Uncoordinated path hopping. In the next phase, $A$ and $D$ hop through
their viable sectors in an uncoordinated fashion. The dwell time on each
sector is sufficient to exchange several probes. If the probes are
successfully exchanged, both devices record the CSI as part of the set used to
generate each fingerprint. Otherwise, the CSI samples are discarded. In Figure
13(b) we show $A$ and $D$ hopping in an uncoordinated fashion between their
viable sectors discovered in Figure 13(a). Only the blue pairs result in
successful probe exchanges that are used for fingerprint generation.

---
(a) Passive adversary close to $D$.
(b) Setup for Jellybean+
Figure 14.  (a) passive threat model and (b) experiment setup.

###  5.1. Security Analysis of Jellybean+

The security of Jellybean+ hinges on the unpredictability of the path that is
sampled at any given time. Because the antennas are independently steered,
Mallory does not know which paths are being sampled, unless she can capture
all probes at all times.

Resistance to passive adversaries. We first consider the case where $M$ is
positioned close to one of the devices, say $D$, such that it can observe
similar viable paths as $D.$ This model is shown in Figure 14(a). This
adversary was shown to be capable of extracting a similar fingerprint as $D$
if only the LoS path was used. To evaluate Jellybean+, we assume the best case
for $M$ where she knows all viable paths identified during the path discovery
process. Referring to the example of Figure 13(a), this would correspond to
knowing that paths 1-5 and 2-4 were discovered. This could be inferred by
either knowing the device topology or intercepting probe messages. To measure
the same fingerprint $F_{D},$ Mallory must hop between the viable paths in an
uncoordinated fashion, similar to $A$ and $D$. The probability of extracting
the same fingerprint equals the probability of following the same path
sequence as $D$ for the fraction of hops that correspond to viable paths
(blue-shaded slots in Figure 13 (b)). This probability is expressed in the
following proposition.

###### Proposition 5.0.

Let $A$ and $D$ hop between $P$ viable paths using uncoordinated sequences
that match at $Q$ hops. The probability that $M$ extracts the same fingerprint
as $D$ is given by

$Pr[F_{M}=F_{D}]=\left(\frac{1}{P}\right)^{Q}.$

###### Proof.

To measure the same fingerprint $F_{D}$, $M$ must steer her antenna to the
same sector as $D$ on all of the $Q$ hops where viable paths are observed.
Each hop in the sequence is randomly selected out of $P$ paths with
probability $1/P.$ Matching a single hop occurs with the same probability of
selecting the specific path out of $P$ paths, which is equivalent to a
Bernoulli trial with success probability $1/P$. Each hop in the hopping
sequence is selected independently, making the probability of matching $X$ out
of $Q$ hops follow the binomial distribution $B(Q,1/P).$ Because we want all
$Q$ hops to match it follows that $Pr[F_{M}=F_{D}]=P(Q)=(1/P)^{Q}.$ ∎

Remark 1: Proposition 1 holds even if Mallory is equipped with $P$ antennas
that can sample physical activity an all $P$ paths simultaneously. This is
because Mallory is not aware of where $D$ steers its antenna at every hop and
therefore she has to guess which of the $P$ paths she should include in the
CSI sample set. A correct guess occurs with probability $1/P$ independently at
each viable hop, yielding Proposition 1.

Remark 2: When relaxing the fingerprint matching condition to only $K$ out of
$Q$ paths, the probability of inferring a similar fingerprint to $F_{D}$ is
given by the binomial distribution $B(Q,1/P)$, with $K$ out of $Q$ successes.

---
Figure 15. Security evaluation of the Jellybean+ when $M$ is co-located with
$D$, where BMR between $A-D$ and $A-M$ as a function of the path dwell time.
Figure 16. Performance evaluation of the Jellybean+ with two types of
activities in experimental room B, where the evaluation metrics BMR, SBR, and
ApEn are captured in a function of the distances of $D$ to the reflector on
the $A-D$ NLoS path.

Note that we cannot analytically derive the probability of inferring the key
$k$ (equivalently the BMR between $F_{D}$ and $F_{M}$) because it depends on
the start time and duration of the observed activities on each path. We
experimentally evaluated the BMR of the adversary under the UPH protocol,
using the setup shown in Figure 14(b). During the path discovery phase, $A$
and $D$ discovered two paths, one through the LoS and one through the
reflector. We collected CSI samples on each of those paths for 25 minutes
independently. We generated hopping sequences at $A$, $D$, and $M$
independently, and varied the dwell time on each path from 0.02 seconds to 1
second. We then followed the uncoordinated path hopping mechanism between $A$
and $D$ according to the generated hopping sequences and recorded the BMR. The
adversary recorded CSI samples, only when it could hear probes from $A$.

Figure 15 shows the BMR as a function of the dwell time on each path. We
observe that the BMR between $F_{A}$ and $F_{D}$ remain unaffected by the
introduction of UPH. On the other hand, the BMR between $F_{A}$ and $F_{M}$ is
approximately 0.5 which equals the probability of guessing the correct path at
every hop. The success probability is also equal to the probability of
randomly guessing $k$, indicating that the adversary gains no advantage by
attempting to observe activities from nearby when path diversity is exploited.

Note that the BMR remains relatively constant for all dwell times. This is
because $M$ hopped paths with the same period as $D.$ Hopping between paths at
a shorter period is not beneficial to the adversary because (a) it will miss
receiving probes that are essential for determining the activity start time
and duration and (b) it still has to guess which paths contribute to the
fingerprints (Remark 1).

Resistance to active adversaries. We now describe how Jellybean+ can prevent
active attacks. An active attack relies on beam-stealing to place Mallory in
control of the probe exchange between $A$ and $D$, so that $M$ can
independently establish keys with $A$ and $D$, using parallel pairing sessions
(MitM). The beam-stealing attack is possible, because the beam alignment
protocol results in the use of a single path (the best path) between two
communicating devices. With UPH, multiple paths are used to extract activity
fingerprints. Even if the adversary controls one or a subset of those paths,
she still needs to guess when $A$ and $D$ sample the remaining attack-free
paths. Moreover, $M$ cannot sample other paths she does not control unless she
simultaneously deploys passive eavesdroppers that are aligned to those paths.
In this case, the results obtained for passive adversaries (Proposition 1 and
Figure 15) hold for the active adversary. As long as there are more than one
paths not controlled by $M$, the probability of matching fingerprints decays
exponentially with the hopping sequence length.

###  5.2. Performance Evaluation of Jellybean+

Time analysis. One concern with UPH is that the uncoordinated hopping will
introduce a long delay until devices can measure sufficiently long
fingerprints. To alleviate this concern, we analyze the time for fingerprint
generation. The path discovery phase is deterministic and requires the
operation of $N$ full sweeps from $D$ where $N$ is the number of available
sector ($N^{2}$ hops in total). In each hop, $A$ and $D$ exchange two probes
(sector sweep frame) and a sweep ACK. Using parameters from the IEEE 802.11ad
specification (IEE, 2012), each probe is 26 bytes long whereas a sweep ACK is
28 bytes long. Moreover, the time interval between two probes for beam
switching, called the short beamforming interframe spacing (SBIFS) is
$T_{s}=1\mu s$. Assuming a 160Mbps transmission rate, the time consumed during
path discovery is $5.12ms$.

The delay for fingerprint extraction during the UPH phase is similar to that
of Jellybean if the dwell time is selected to be low (e.g., less than 0.1sec).
This is because path hopping is much faster than the activity timescale, so
that all activities occurring on any of the viable paths will be
intermittently sampled.

Performance on the reflection path. Since Jellybean+ uses a reflection path,
we explored the the BMR, SBR, and ApEn on that path. We varied the distance
between $D$ and the reflector as depicted in room B of Figure 5(c). The
experiments were executed for 3 minutes under each distance with artificial
and natural daily activities. Figure 16 shows the BMR, SBR, and APEn for the
two types of activities. We observe that the BMR, SBR, and ApEn have similar
values to those obtained in the LoS path for each activity type, despite the
lower RSS on the reflected path. The use of CSI variance is a strong indicator
of activity and inactivity. In fact, the reflected path is easier to disrupt
due to its higher attenuation.

##  6\. Related Work

Context-based Device Pairing. Our proposed protocol belongs to the general
category of context-based device pairing protocols. Such protocols aim to
establish a shared secret key between two wireless devices by sensing common
contexts that are dynamic and random, without relying on any pre-shared
secrets such as passcodes. They provide notable advantages such as reduced
user efforts, perpetual key renewal, and a high level of security against
remote adversaries. Commonly explored physical modalities in context-based
pairing include ambient light (Miettinen et al., 2014), sound (Xu et al.,
2021b; Miettinen et al., 2014; Schürmann and Sigg, 2011), human operation (Li
et al., 2020; Zhang et al., 2018). However, all the above methods require that
all the devices possess the same sensing modality, which can be impractical
for real-world applications such as IoT. Recently, several works addressed
this limitation by using multi-modal sensing (Han et al., 2018; Farrukh et
al., 2022) for common activity detection, which is applicable to devices with
heterogeneous sensing capabilities. However, the types of activities that can
be simultaneously sensed by different sensors are limited. Most importantly,
all those methods are only secure against a passive adversary which is located
outside of the physical context (e.g., a room where the legitimate devices are
located).

On the other hand, other works such as (Liu et al., 2013; Mathur et al., 2008,
2011; Jana et al., 2009; Premnath et al., 2012; Liu et al., 2012b; Zeng et
al., 2010) used the (in-band) radio interface to sense ambient RF signals do
not require devices to possess a common sensor other than the same RF
interface which is widely existed. They have leveraged the correlation of
measured ambient RF signals (e.g., RSS) to securely pair two or more devices
that are co-located or in proximity (Mathur et al., 2011; Shi et al., 2012; Wu
et al., 2018). However, RF signals decorrelate fast when the distance
increases (usually not correlated beyond half wavelength), which requires very
small proximity ranges (e.g., 12.5 cm at 2.4GHz, and millimeter range for
mmWave frequencies) that are impractical for many applications. In (Xu et al.,
2022), large-scale fading of RF signals is leveraged for verification of
continuous proximity, which has a larger correlation distance, but it is
applicable to outdoor mobile settings and not in the mmWave band. In contrast,
our protocol does not limit the distance between the legitimate devices as
long as they are within each other’s communication range. Also, it is secure
against an adversary in the same environment (even in close proximity to
legitimate devices).

Security of mmWave Communications. Early works on the security of mmWave
focused on ensuring message confidentiality against passive adversaries.
mmWave is generally considered to be more secure against eavesdropping due to
its highly directional transmissions. Indeed, this is the case when the
adversary is located outside of the beam of a mmWave link. Yet, Steinmetzer et
al. (Steinmetzer et al., 2015) demonstrated that eavesdropping is still
possible if the adversary can place a reflector within the beam to deflect the
signal. There are also works that leverage channel reciprocity to extract
secret keys from a mmWave link (Jiao et al., 2018; Li et al., 2019; Jiao et
al., 2019), however, it is challenging since the entropy is low under static
channel environments, while channel reciprocity may not hold for dynamic
channels due to the high sensitivity of mmWave signals.

On the other hand, several works studied the security of mmWave communication
against active attacks such as impersonation or MitM. Balakrishnan et al.
(Balakrishnan et al., 2019a) studied device fingerprinting based on variations
in their beam patterns, in order to prevent impersonation attacks. Wang et al.
(Wang et al., 2021a, 2020) adopted a similar approach by using machine
learning algorithms to extract unique beam patterns for each device, enabling
the receiver to distinguish devices and detect spoofing attacks. However,
these methods require training, which incur extra overhead and complexity, and
it is impractical to obtain secure training data when new devices join the
network. Steinmetzer et al. (Steinmetzer et al., 2018a) first demonstrated the
beam-stealing attack against the 802.11ad protocol for mmWave communications,
and proposed an authenticated beam sweep protocol that integrates crypto
machinery to protect it against beam-stealing. However, in the device pairing
problem, there is no prior trust which makes such an approach inapplicable.

Wireless Sensing for Human Activity Detection and Recognition (HAR). Our work
is also related to wireless sensing, especially passive sensing where the
wireless transceivers are deployed in the environment rather than on the
objects of interest. Existing works in this domain typically extract features
from the CSI of the received wireless signals to detect or recognize human
activity, based on the fact that different activities will incur different
disturbances to the CSI. For example, in sub-6GHz bands, WiFi signals have
been widely used for activity sensing (Venkatnarayan et al., 2018; Gao et al.,
2021; Tan and Yang, 2016; Wang et al., 2014, 2017). Meanwhile, due to the high
bandwidth and sensing resolution of mmWave signals, mmWave sensing has
attracted increasing attention in recent years (Liu et al., 2020; Singh et
al., 2019; Wang et al., 2021b; Sun et al., 2019; Jiang et al., 2018;
Vandersmissen et al., 2020; Lien et al., 2016; Wang et al., 2016).

Our work differs from HAR, since our activity fingerprinting method only aims
at detecting the presence or absence (timing) of an activity, rather than
classifying the type of activity. In contrast, human activity recognition
typically relies on machine learning methods which require a large amount of
training data. It is impractical to collect sufficient and secure training
data in device pairing scenarios (where the devices meet for the first time
without prior trust). In our protocol, we only require that the devices can
observe common events’ timing, thus training is not needed.

##  7\. Conclusion

We proposed Jellybean, a novel secret-free pairing protocol for devices
operating in the mmWave band. Our basic idea is to leverage in-band mmWave
communications to detect (human) activities present in the physical
environment as the common context. Jellybean extracts activity timing
information via CSI measurements, which is used as a source of shared entropy
for key agreement. We further enhance the security of the basic scheme with an
uncoordinated path hopping mechanism, which achieves activity detection using
directional beams under the absence of prior trust. The key novelty of
Jellybean originates from exploiting the unique properties of mmWave signals
to defend against both co-located passive adversaries as well as active
adversaries. We evaluated Jellybean experimentally in two indoor environments,
and showed that it can securely pair devices in the same room while thwarting
adversaries located in the same space.

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