Analysis of Payload Confidentiality for the IoT/ LPWAN

Technology ‘Lora’

Bernard McWeeney, Ilya Mudritskiy and Renaat Verbruggen

School of Computing, Dublin City University, Dublin, Ireland

Keywords: LPWAN, IoT, LoRa, SCADA, P2P Communication, Payload Confidentiality, SDR, AES, Cryptography.

Abstract: Climate change necessitates a transition towards renewable energy sources like solar panels and wind turbines.

The integration of the Internet of Things (IoT) has been pivotal in achieving these advances, offering the

potential to optimise renewable energy system performance and efficiency through real-time data collection

and predictive maintenance. Prominently, Low Power Wide Area Network (LPWAN) technologies like LoRa

are aiding this transition, providing IoT with extended coverage, reduced infrastructure complexity, and

ensuring low power consumption. With IoT playing a central role in critical infrastructure, secure

communication is crucial to protect against potential cyber threats. Maintaining the integrity of sensitive data

relayed through IoT devices is paramount. We provide an in-depth analysis of payload confidentiality in LoRa

point-to-point (P2P) communication within remote smart grids. We explore the integration of IoT hardware

encryption features and the implementation of user-controlled Advanced Encryption Standard (AES)

algorithms on ESP32. We propose a robust solution for secure P2P communication using AES cryptography

on ESP32 with LoRa. It is feasible to integrate end-to-end payload confidentiality in LoRa P2P

communication. This study offers secure communication in remote smart grids, valuable insights into trade-

offs and potential security risks in implementing LoRa P2P in IoT applications.

1 INTRODUCTION

Climate change, one of the most critical issues facing

our world today, necessitates urgent action to protect

global environmental sustainability and human

livelihoods. Central to this challenge are the United

Nations' Sustainable Development Goals (SDGs),

particularly Goals 7, 11, and 13, which advocate for

affordable and reliable energy, sustainable cities and

communities, and prompt action to combat climate

change (United Nations, 2023).

Addressing these pressing concerns requires a

global transition towards renewable energy sources,

fostering the advent of distributed energy generation

and remote grids. This revolution in energy generation

and distribution democratises access to resources,

significantly reducing fossil fuel dependence

(GIoTitsas, 2015). Here, the Internet of Things (IoT)

plays a pivotal role, enabling the development of smart

grids for real- time monitoring and control of energy

production, distribution, and consumption (Khan,

2020). Thus, IoT-powered smart grids enhance

renewable energy systems' efficiency and cost-

effectiveness, balancing the intermittent nature of

solar and wind power.

However, the integration of IoT into energy

infrastructures presents its challenges. Data security

becomes a paramount issue, particularly within the

energy sector. A breach could result in grid instability

or widespread blackouts, making the scrutiny of IoT

device security vulnerabilities in this context critical.

A crucial component of these micro-grid

networks is the SCADA system, ensuring power

quality control. Historically, the lack of low-cost,

secure, and authentic communication systems with

minimal power consumption posed significant

hurdles for SCADA systems within Smart Grids

(Rizzetti et al., 2015), (Tawde, 2015). The Russian

army's exploitation of the Georgian electric grid's

SCADA system in 2008 (Fillatre, 2017) and the

disruption of the U.S electrical grid control system in

2009 underline the importance of grid

communication security. A secure communication

system, therefore, necessitates message

authentication, integrity, availability, and

confidentiality.

McWeeney, B., Mudritskiy, I. and Verbruggen, R.

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’.

DOI: 10.5220/0012271400003648

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information Systems Secur ity and Privacy (ICISSP 2024), pages 90-102

ISBN: 978-989-758-683-5; ISSN: 2184-4356

The advent of long-range wireless technologies

and LPWANs provides solutions to these challenges,

enabling long-range communications at a low bit rate

among interconnected devices like battery-operated

sensors (Petajajarvi, 2015). LPWAN technologies

streamline the deployment process and offer extensive

coverage areas, spanning several kilometres (Mekki,

2019). Prominent LPWAN technologies such as

LoRa and SigFox have found applications in fields

like smart grids and factory monitoring (Patil, 2022),

where robust device security against potential threats

is of utmost importance. These technologies allow

individual devices to cover large distances, thus

significantly reducing costs by eliminating the need

for intermediate routing nodes.

LoRa (Long Range), a patented digital wireless

data communication IoT technology developed by

Cycleo (later acquired by Semtech), has positioned

itself as a leading IoT service provider worldwide

(Ahmadi, 2018). LoRa operates in the unlicensed sub-

GHz ISM radio band, depending on the deployed

regions (e.g., 863–870MHz in Europe, 902– 928MHz

in the USA), but also obliges to the duty cycle

regulations (e.g., 1% in Europe) (LoRa, 2023).

Compared with short- range wireless standards and

cellular technologies, LoRa shows remarkable results

in long-range transmission and ultra-low power

consumption. Specifically, its coverage range is up to

15 km (rural areas) and 5 km (urban areas) (Magrin,

2017), its device battery life is up to 10 years

(Ferreira, 2020), and its data rate ranges from 0.3 to

37.5Kbps (Rawat, 2020). LoRa is rooted in spread

spectrum modulation techniques derived from chirp

spread spectrum (CSS) technology (Poursafar, 2017).

A typical LoRa network comprises end devices,

gateways, and a network server implementing the

long-range wide-area network (LoRaWAN)

protocol. LoRaWAN, a cloud-based medium access

control, primarily acts as the network layer protocol,

managing communication between LPWAN

gateways and end-node devices as a routing protocol

(Devalal, 2018). Here, end devices handle the

physical layer process, while gateways function as

repeaters, collecting data from different end devices

(Almuhaya, 2022

Despite extensive deployment, IoT nodes, often

located in remote areas, continue to face limited

coverage. Additionally, the high subscription cost per

node and the unnecessary infrastructure investment

for applications not requiring extensive antenna and

gateway setups led to the development of LoRa point-

to-point (P2P) systems (Sinha, 2017). These systems

allow for simple deployment and have demonstrated

their utility in sectors such as agriculture, renewable

energy monitoring, and logistics and transportation

(Iqbal A. and Iqbal T., 2018; Chen et al., 2019;

Setiabudi et al., 2022).

While numerous research papers have

investigated the performance of P2P LoRa End

Device Communication and analysed propagation

measurements for LoRa networks in urban

environments (Callebaut, 2019), (Iqbal, 2018),

(Chen, 2019) , the security implications of employing

a P2P LoRa communication link are often

overlooked.

With these considerations, this paper offers a

thorough end-to-end analysis of payload

confidentiality in LoRa P2P communication within

remote smart grids. We propose the deployment of

algorithms to augment payload security in a P2P

network. We explore the integration of IoT hardware

encryption features and the implementation of user-

controlled Advanced Encryption Standard (AES)

algorithms on ESP32 using different modes. In

addressing security concerns in SCADA systems, we

propose a robust solution for secure P2P

communication using AES-128 using CMAC CBC

mode on ESP32 with two low cost LoRa devices.

The paper is structured as follows: Section two

offers a review of relevant literature in LoRa and IoT

security; section three outlines the research focus;

section four details the methodology; section five

identifies key components central to the research,

including the experimental environments; section six

presents the results; section seven compares the

coverage and security standards of LoRa with other

LPWAN providers; and the final section offers a

conclusion, summarising the work and providing

closing observations.

2 LITERATURE REVIEW

We Secure transmission of data is critical for the

development and usability of IoT services in the smart

grid. Within the context of the IoT domain, the

research concentrates on LoRa and IoT

communications security.

Focusing on IoT SCADA systems, the real-world

application of LoRa P2P, the vulnerabilities of LoRa

physical as well as the studies done on LoRa P2P

encryption. A comprehensive review of current

research in the area was conducted and some of the

key papers and findings from this review are

discussed in this section. In this 2022 study, Setiabudi

et al (Setiabudi, 2022) develop a system which

monitors and controls the speed and vibration of a

wind turbine using an Arduino Uno microcontroller

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’

and Bolt IoT WI-FI Module. The paper demonstrates

that it can effectively control and monitor Wind

Turbines from remote locations utilising the Bolt

cloud server which processes the data. Similarly in

this 2019 paper, Chen et al (Chen, 2019, develop an

open-source SCADA system for solar photovoltaic

monitoring and remote control. Again, it

demonstrates that IoT SCADA systems can be used

to replace traditional SCADA systems found in solar

or wind energy generation systems. However, the use

of short-range communication links such as Wi-Fi

limits the flexibility of these approaches for off-grid

or remote location.

The study Setiabudi et al (Setiabudi, 2022)],

develop an alternative approach to a Wi-Fi

communication for Solar Panel monitoring. They

designed a wireless sensor network using P2P LoRa

nodes and developed a waiting protocol to help

improve packet reception. They integrate thinger.io

website as a real-time monitoring medium. They were

able to transmit packets over a distance of 3km.

However, they neglect to assess any security

implications of using LoRa communication which is

an important aspect of energy infrastructure.

Likewise in the recent 2023 study, Pradeep (Pradap

et. al, 2022), presents an approach for detecting forest

fires by using a ESP32 microcontrollers, fire detector

sensors and LoRa P2P communication between the

two ESP32 nodes.

While the results clearly illustrate the real-life

application of LoRa P2P communication, the study

does not provide any analysis on the need for

encryption or applies encryption to the

communication link.

LoRa P2P utilises the LoRa physical layer for

communication, however serval studies have

highlighted vulnerabilities in the layer. Focusing on

the confidentiality aspects of the CIA triad, a common

confidentiality vulnerability is the challenge of Chirp

Spread Spectrum modulation where confidentiality

cannot be guaranteed (Paredes, 2019). In the study

they were able to record raw transmission of LoRa

physical layer using an inexpensive software defined

radio (SDR). Using the experimental gr- LoRa out-

of-tree module from Bastille Research, they were able

to demodulate and decode the LoRa transmission

using a GNU radio. They stress the need for securing

the LoRa physical layer. They also discuss several

common LoRa radio vulnerabilities such as the

“Packet in Packet” attack and Jamming attacks. The

authors conclude by outlining future work on “further

improving the physical layer”. The paper does not

provide any means of encryption of the LoRa physical

layer but stresses its importance for future work.

The study Pradap et al (Pradap, 2022) , proposes a

secure long-range communication model using LoRa

(Long Range) technology and ESP32

microcontrollers The study concludes that LoRa

communication is possible over distances of 10-15km

with minimal to no data loss, making it suitable for

peer-to-peer communication style. The results clearly

illustrate LoRa P2P encrypted transmission, but the

study does not define the power consumption of the

additional edge technology required for

implementing this encryption, an important

consideration of LPWAN and only shows AES basic

encryption modes and a weak Caesar cipher methods

implementation.

The paper of Ali et al. (Ali, 2022) explores a

secure, low-cost communication system for remote

micro-grids, implemented with AES cryptography on

ESP32, utilising a LoRa module several

cryptographic techniques are explored and compared,

with the authors concluding that AES provides the

most robust security for SCADA systems. The paper

highlights the implementation of AES on the ESP32

with LoRa, explaining the steps and security of the

algorithm. Test results affirm the system's security

and cost- effectiveness, providing a compelling

solution to communication challenges in remote

micro-grids. However, the authors did not implement

or provide a comparison of other AES encryption

modes using only the basic AES encryption.

3 RESEARCH QUESTIONS

T The purpose of this paper was to present security

issues of LoRa P2P regarding the payload

confidentiality of the transmission, more specifically

related to smart grids. The research questions to be

answered were:

How does the physical layer (PHY) of LoRa

implement device and network security for data

transmission, and what are its strengths and

weaknesses?

What capacities exist to enhance the security of

P2P nodes in a LoRa network, and what would be the

potential barriers and facilitators to such

enhancements?

What potential encryption algorithms could be

developed and tested for the integration of end-to-end

payload confidentiality in LoRa P2P communication

within remote smart grids?

What are the potential performance trade-offs

when integrating end-to-end payload confidentiality

in LoRa P2P communication within remote smart

grids?

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

How does the integration of IoT hardware

encryption features impact the security of LoRa P2P

communication in remote smart grids?

4 METHODOLOGY

The procedure for this research unfolded

systematically across several defined phases, each

with a specific focus and purpose. Here, we elucidate

each of these stages.

A. Research Topic Identification

The study is centred on building secure low-cost point

to point data networks and examining the associated

security challenges.

B. IoT Research and Selection

After outlining the scope, the next step was selecting

an apt IoT device and communication network that

would satisfy the intricate demands of SCADA

communication. A thorough investigation was

conducted to ensure an appropriate selection.

C. Literature Review

The comprehensive literature review revealed the

enormity of IoT security issues. The review

scrutinised numerous journals on various themes such

as IoT SCADA systems, IoT communication, security

of P2P nodes, software- defined radio, and data capture

and demodulation. This helped refine the scope of the

research, and specifically addressed research

questions around security and encryption.

D. Environment configuration and IoT setup

To yield accurate results, it was necessary to ensure

that quality experiments were defined and quality IoT

devices and testing tools were used. IoT assembly;

device integration; software functionality testing and

upgrade; tool testing and selection; data capture and

demodulation; C development were among the key

challenges.

E. Configuration of Software Defined Radio (SDR)

Data capture and analysis necessitated extensive tool

research and testing. The process involved identifying

individual functionality requirements for SDR

capture, signal playback, demodulation, and their

integration and the technical challenges of integrating

the software application with the devices.

F. Transmission Capture and Demodulation

Test messages of varying payload lengths were

designed and generated for LoRa. The payloads were

transmitted. The messages were captured during

transmission in the .wav demodulation format.

Demodulation of the dataset was performed, to assess

potential message vulnerability and payload plaintext

exposure.

G. Payload Analysis

Post-demodulation, the output was analysed with an

emphasis on the payload. Recognisable patterns

between known plaintext input and output were

explored. Received messages on the LoRa node were

included to provide a comprehensive end to end

payload analysis. Visual identification of the captured

transmission was reviewed to add to the insights.

H. Payload Encryption

Informed by the payload analysis, encryption

mechanisms and algorithms were evaluated. We

developed a mix of inherent services and user-level

encryption solutions using the AES-128 algorithms to

ensure data confidentiality on ESP32 LoRa devices.

The test message data from Phase F was used as input

data for each encryption algorithm, enabling a broad

comparative analysis.

I. Results Evaluation

Data transmitted via ESP32 LoRa devices were

captured, demodulated, and compiled for

interpretation. The ciphertext data was cross-checked

against the known plaintext input and the LoRa

message structure. This was then analysed for

patterns and vulnerabilities.

J. IoT LPWAN Benchmarking

Finally, a benchmarking analysis was conducted,

comparing the LoRa IoT protocol against other

similar LPWAN technologies. This step helped

determine the standards and quality of security

adopted by LoRa and its market competitors.

5 EXPERIMENTS

This section presents the development and

implementation of the experiments. Two main

experiments were conducted. Experiment one: LoRa

transmission signal capture, demodulation, and

analysis, involving 2 sub- experiments of pre and

post encryption. Experiment two: Payload

encryption and packet security, involving three sub-

experiments using different AES encryption

algorithms.

Each of the two main experiments required a

different combination of software and hardware. The

Heltec ESP32 V3 LoRa devices were common

across all experiments.

Heltec ESP32 V3 LoRa devices were specifically

chosen for the study based on them supporting the

LoRa protocol and them being referenced in the

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’

LoRaWAN official network operator “The Things

Network” as a supported LoRaWAN Node

(Triwidyastuti, 2019) . The Heltec ESP32 V3 LoRa

devices also provide other network connectivity

options of Wi-Fi and Bluetooth, but these were not

utilised (Heltec, 2023).

Analysis conducted by Fatima, et al. 2019,

pertaining to LoRa’s Chirp Spread Spectrum

modulation, reveals LoRa signal transmission

confidentiality cannot be guaranteed. In the study Hill

et al 2019 (Hill, 2019), relating to LoRa’s application

of integrity and authentication between P2P nodes,

reveals LoRa is vulnerable to “Packet in Packet”

attack and Jamming attacks and in the 2017 study of

Aras et al revealed several vulnerabilities and stresses

that LoRa “has serious security vulnerabilities that

can be exploited by malicious third entities” (Taha,

2021). The LoRa P2P physical layer does not have

any encryption, meaning the confidentiality of the

payload is optional and up to developers and or

manufacturers to implement security features of the

LoRa physical layer (GK S., 2022).

Executing the experiments of the signal capture

and demodulation and with the various encryption

algorithms, required a constant test dataset predefined

beforehand. The test dataset was constructed of

plaintext payloads of various bytes. The different

payload lengths were to test the boundaries of the

payload and included special characters. The test

dataset is presented in Table 1.

Table 1: Test dataset.

# of

bytes

Plaintext payload Plaintext in Hexadecimal

1 A 41

3 1A2 31 41 32

5 @1B2C 40 31 42 32 43

10 Ab12CD34@. 41 62 31 32 43 44 33 34 40 2E

15 mp047..!bd260h1 6D 70 30 34 37 2E 2E 21 62 64 32

36 30 68 31

5.1 Experiment 1

Experiment 1 performed a capture and evaluation of

a LoRa transmission between two P2P nodes. The

experiment setup apparatus seen in Figure 1,

consisted of two Heltec ESP32 V3 LoRa devices (a

sender and a receiver node), (Heltec, 2023) and a

Digital RTL-SDR capable of capturing transmissions

of DVB-T,FM,DAB,820T2 and LoRa. The software

applications necessary to execute the experiments

included custom Arduino C code with reference to

Heltec manufacture documentation , for the sender

and receiver nodes using the configuration settings

outlined in Table 2. CubicSDR, an open-source

software defined radio application was used for the

transmission signal capture and audio file output,

mainly used for visual analysis (CubicSDR, 2023).

For demodulation of the LoRa signal capture, the

open source Gqrx application (Gqrx, 2023) was used

for capturing the transmission in .raw format. The

open- source GNU Radio application (GNU Radio,

2023), the gr-sigmf out-of-tree (OOT) module for

reading and writing SigMF datasets (SkySafe, 2023)

and the gr-LoRa OOT module (RPP0, 2023) of GNU

Radio blocks for the actual demodulation of the LoRa

signal were utilised.

The Heltec ESP32 V3 LoRa devices was

programmed, with one to transmit the test dataset and

the other one to receive the payloads. The devices

were programmed to transmit on the frequency

channel of 867.9 MHz’s. CubicSDR was tuned to the

LoRa frequency of 867.9 MHz and to recording mode

for capturing .wav files. We utilised CubicSDR for its

visual analysis of the transmission. However .wav file

format is not great for demodulation of a LoRa signal.

A better data format for radio frequencies (RF) is the

sigmf data format (Hilburn, 2017) This required

capturing the signal in a .raw file format. Gqrx

application was used for capturing the signal and

providing the data in Raw format.

The LoRa sender device transmitted the

individual message and the LoRa transmission was

picked up by Gqrx, where it was centring on each

individual LoRa signal and recorded the transmission

in Raw format. To convert the .raw file into the two

sigmf data formats (.sigmf-data & .sigmf- meta)

required the use of the GNU Radio application. GNU

radio application provides signal processing blocks.

We created a flow graph using a combination of

blocks provided by gr-sigmf and GNU Radio to

convert the Raw to .sigmf datasets.

Gr-LoRa, an open-source physical layer

implementation of the LoRa protocol which supports

demodulation of LoRa signals, was used to process

the .sigmf datasets for demodulation and analysis.

Table 2: LoRa Configuration settings.

Configuration Type Configuration Value

Radio Frequency 867.9 MHz

Output Power 10

Bandwidth 125

Spreading Factor (SF) 12

Preamble Length 8

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

Figure 1: LoRa signal capture experiment apparatus.

5.2 Experiment 2

In Experiment 2, a sequence of tests was conducted

to investigate the secure preparation, encryption, and

transmission of messages within the ESP32-based

LoRa communication system for a LoRa P2P

network. For this purpose, we utilised AES

encryption algorithms to protect the confidentiality of

transmitted data.

The Advanced Encryption Standard (AES) is a

globally accepted symmetric encryption algorithm

(Rijmen, 2001). It can operate with a key length of

128, 192, or 256 bits, and a fixed block size of 16

bytes. It supports various modes of operation,

including Electronic Code Book (ECB), Cipher

Block Chaining (CBC), Cipher Feedback (CFB),

Output Feedback (OFB), and Counter (CTR), each

with its own security characteristics and use cases.

For our experiment, AES-128 was selected and a

range of operation modes were experimented; ECB,

CTR & CBC. Compared to AES-256 and RSA, AES-

128 uses less resources and does not require high

computational power for encryption and decryption,

important for LPWAN devices . The libraries we used

to implement AES-128 were verified against the data

in the National Institute of Standards & Technology

(NIST, 2001)

Using a minimal sized AES encryption library for

embedded systems and low power chips was an

important consideration for LPWANS. Using the

tiny-AES-c library (KOKKE, 2023) the tiny-AES-

CMAC-c library (Elektronika-ba,, 2023) and the

Arduino c library (Arduino-libraraies, 2023). The

tiny-AES-c library implemented all 3 modes of AES-

128 encryption in a compact and small library useful

for embedded systems. However, with this library

there are no safety mechanisms for typical C and C++

issues such out of bounds memory access which

required extra development work to implement in the

embedded system.

1) AES – Electronic Code Book (ECB):

In ECB mode, each block of plaintext is encrypted

independently using the same key. It's simple and

fast but offers the least security among the modes.

This is because identical plaintext blocks produce

identical ciphertext blocks, making it susceptible to

pattern attacks.

2) AES – Counter (CTR)

CTR mode effectively turns the block cipher into a

stream cipher. It involves encrypting a sequence of

incrementing counters (initially seeded with a nonce)

with the cipher's key, and the resulting cipher stream

is XORed with the plaintext to get the ciphertext. It's

parallelisable and it supports input data of any length,

and padding is not required (Conti, 2017). As padding

is not required it saves on the size of the message,

improving the transmission performance.

3) AES – Cipher Block Chaining (CBC)

CBC mode introduces dependency between blocks

for increased security. Each plaintext block is XORed

with the previous ciphertext block before being

encrypted. An Initialisation Vector (IV) is needed for

the first block. Due to chaining, it's not parallelisable

and requires padding for non- multiple block size

inputs. The specific key and IV values need to be

managed and securely updated as part of our overall

system design .

4) AES – CBC with Cipher-based message

authentication code (CMAC)

In this setup, not only is the data encrypted using CBC

mode, but an AES-CMAC is also computed for

message authentication. CMAC is a type of Message

Authentication Code (MAC) that provides assurance

of data integrity and authenticity. The CMAC is

computed over the data and appended to the end of

the message, allowing the receiver to verify the

integrity of the message upon decryption . The tiny-

AES-CMAC-c library is flexible because it uses the

original encryption functions as a call-back to

calculate the digest of the original message.

Every subset of Experiment 2 was re-examined

using the procedures from Experiment 1, which

involved capturing the LoRa transmission signal,

demodulating it, and analysing the executed code.

Each subset experiment required the Heltec ESP32

V3 LoRa sender device to be flashed with the selected

encryption algorithm.offers the least security among

the modes. This is because identical plaintext blocks

produce identical ciphertext blocks, making it

susceptible to pattern attacks.

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’

5) AES – Counter (CTR)

CTR mode effectively turns the block cipher into a

stream cipher. It involves encrypting a sequence of

incrementing counters (initially seeded with a nonce)

with the cipher's key, and the resulting cipher stream

is XORed with the plaintext to get the ciphertext. It's

parallelisable and it supports input data of any length,

and padding is not required (Lipmaa et al., 2020). As

padding is not required it saves on the size of the

message, improving the transmission performance.

6) AES – Cipher Block Chaining (CBC)

CBC mode introduces dependency between blocks

for increased security. Each plaintext block is XORed

with the previous ciphertext block before being

encrypted. An Initialisation Vector (IV) is needed for

the first block. Due to chaining, it's not parallelisable

and requires padding for non- multiple block size

inputs. The specific key and IV values need to be

managed and securely updated as part of our overall

system design (Almuhammadi and Al-Hejri, 2017).

7) AES – CBC with Cipher-based Message

Authentication Code (CMAC)

In this setup, not only is the data encrypted using CBC

mode, but an AES-CMAC is also computed for

message authentication. CMAC is a type of Message

Authentication Code (MAC) that provides assurance

of data integrity and authenticity. The CMAC is

computed over the data and appended to the end of the

message, allowing the receiver to verify the integrity of

the message upon decryption (Stanco, 2022)( The tiny-

AES-CMAC-c library is flexible because it uses the

original encryption functions as a call-back to calculate

the digest of the original message.

Every subset of Experiment 2 was re-examined

using the procedures from Experiment 1, which

involved capturing the LoRa transmission signal,

demodulating it, and analysing the executed code.

Each subset experiment required the Heltec ESP32

V3 LoRa sender device to be flashed with the selected

encryption algorithm.

6 RESULTS

6.1 Experiment One: LoRa

Transmission Signal Capture,

Demodulation and Analysis

The following section presents the outcomes from the

LoRa data capture and analysis experiment. Output

data are presented in the form of demodulated

transmissions.

6.1.1 Visual Analysis

For each LoRa message transmitted, it can be

observed in the Cubic SDR waterfall which visualises

the frequency content of a signal over time, each

individual chirp and the make-up of the transmission.

Chirp Spread Spectrum (CSS) modulation, as

used in LoRa, is designed to enhance communication

robustness, even in challenging conditions such as

high noise environments, signal fading, or high

network density. This is largely because CSS

modulates the signal across a range of frequencies,

which can make it more resistant to interference and

noise that might be present at a single frequency.

The Heltec ESP32 V3 LoRa sender device

transmission appears as a series of 'chirps'. These

'chirps' sweep across a range of frequencies.

Depending on the spreading factor (SF) used, the

chirp can be wider or narrower, slower, or faster. In

our case we used a SF of 12 which is the highest

spread factor so you can clearly see the thickness of

the individual chirp across the frequency spectrum

range.

Figure 2: LoRa transmission waterfall captured by an SDR.

The Bandwidth of a LoRa signal is the range of

frequencies over which a chirp sweeps. A higher

bandwidth means that the chirp is longer in frequency,

which shows up as a longer line in the waterfall

display. These characteristics of the LoRa transmission

help the signals get through high density spectrum.

The waterfall also visibly shows the preamble and

Start of Frame. A continuous number of up-chirps is

the preamble, and two down chirps is the start of

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

Table 3: LoRa Phy frame structure.

LoRa Phy Frame

Phy

Frame

Preamble

PHDR (Physical

Header)

PHDR_CRC

(Header Cyclic Redundancy

Check)

Phy Payload

CRC

Size

Min 4.25 symbols 2 bytes 2 bytes Max. 255 bytes 2 bytes

Coding rate = 4/8 Coding rate = 4/(4 + CR), where CR = 0,1,2,3 or 4

Table 4: LoRa p2p demodulation findings.

of bytes Plaintext Payload Captured Transmission Demodulated Converted to Ascii

1 A 41 A

3 1A2 31 41 32 1A2

5 @1B2C 40 31 42 32 43 @1B2C

10 Ab12CD34@. 41 62 31 32 43 44 33 34 40 2E Ab12CD34@.

15 mp047..!bd260h1 6D 70 30 34 37 2E 2E 21 62 64 32 36 30 68 31 mp047..!bd260h1

frame delimiter, and these indicate the start of the data

frame.

Signal Capture and Demodulation

The Gqrx captured the Raw transmission. Using the

GNU Radio to interface with the hardware SDR

performed signal processing on the input

transmission. The Gqrx Raw transmission was

converted to the .sigmf format and then piped into the

gr-LoRa application where demodulation of the LoRa

transmission was performed as programmed.

Table 4 presents an analysis of findings from

LoRa message transmission utilising the transmission

configuration outlined in Table 2. The Gr-LoRa

captured and demodulated payload content can be

easily converted from its hexadecimal form to ASCII,

revealing the original plaintext message.

6.2 Experiment Two: Payload

Encryption

Having confirmed that LoRa P2P communication

defaults to unencrypted payload transmission in

Experiment 1, Experiment 2 consisted of multiple

experiments integrating and testing the different AES

algorithms in the Heltec ESP32 V3 LoRa devices.

Performance cost and payload encryption quality

were key considerations of the algorithm analysis.

1) AES – ECB Mode

In the first experiment, we implemented AES in ECB

mode. ECB's simplicity became evident: each

plaintext block is encrypted separately, making it a

highly efficient mode. This independent block

encryption allowed for a high-speed operation, with a

processing time of just 49179 µs, a characteristic

particularly beneficial for real-time systems. As ECB

does not require Initialisation Vectors (IV), it reduces

the overhead on resource limited IoT devices.

However, the efficiency and simplicity of ECB mode

comes with its own vulnerabilities. The deterministic

nature of the encryption means the same plaintext

block always yields the same ciphertext block when

encrypted with the same key. This was empirically

confirmed by our experiments, where identical

plaintext messages consistently yielded identical

ciphertext blocks, as seen in the ECB Mode of Table

6. This makes it susceptible to frequency analysis and

pattern detection attacks.

2) AES – CTR Mode

The second experiment involved the implementation

of AES in CTR mode. CTR, a type of stream cipher,

encrypts a counter value initialised with a nonce rather

than the plaintext itself. The resulting encrypted

counter value is then XORed with the plaintext to

produce the ciphertext. This approach breaks the

deterministic relationship between the plaintext and

the ciphertext, making it more challenging to detect

patterns in the encrypted data. However,

implementing CTR mode requires robust key and

counter management solutions to maintain

synchronisation. In our experiments, we encountered

issues with key and counter synchronisation between

the two devices, leading to failed transmissions or

rejected messages, potentially due to system clock

differences of the sender and receiver nodes or due to

issues with the integration of the AES library with the

Arduino library, we could not accurately narrow

down the problem.

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’

3) AES – CBC Mode

Our third experiment explored the AES in CBC mode.

Unlike ECB and CTR, CBC introduces an inter-block

dependency. Each plaintext block is XORed with the

previous ciphertext block before being encrypted. This

inter- block dependency effectively mitigates the issue

of pattern detection present in ECB, yielding better

data confidentiality. Our results, displayed in the CBC

Mode of Table 7, demonstrate this benefit: identical

plaintext blocks, when encrypted with the same key,

produced different ciphertext blocks. However, CBC

mode requires the use of an Initialisation Vector (IV)

to enable this benefit, adding to the system complexity.

4) AES – CBC Mode with CMAC

In our final experiment, we appended an AES-CMAC

(Cipher-based Message Authentication Code) to our

CBC- encrypted messages. CMAC is a type of MAC

that provides assurance of data integrity and

authenticity. With CBC- CMAC, our LoRa P2P

communication system offers both data confidentiality

and integrity. This extra layer of security introduced

additional computational overhead. As evidenced by

the increase in processing time in our results to 65136

µs, it's clear that this mode, while secure, demands

more processing resources although minimal.

Comparing CBC and CBC-CMAC in the power

consumption table, it is evident that the power

consumption is slightly higher when CMAC is used,

registering 0.06839 µW due to the additional

computational steps involved in generating the MAC.

The addition of the CMAC also appended more bytes

to the message transmission. Our experiments revealed

that the choice of encryption mode significantly

impacts the confidentiality, data integrity, performance

efficiency, and resource requirements of a LoRa P2P

system. It highlighted the fact that there's no 'one-size-

fits-all' solution and that a careful balance between

these factors needs to be struck depending on the

specific requirements of the system in question. In our

case for P2P remote grid connections, CBC-CMAC

offers the best security among the options tested.

Table 5: Encryption processing times and power

consumption.

Plaintext ECB CBC CBC-

CMAC

Processing

Time

49179 µs

50740 µs 54581 µs 65136 µs

Power

Consumption

0.03408

µW

0.068392

µW

0.05731

µW.

0.06839

µW

7 LPWAN IoT BENCHMARKING

LPWAN benchmarking was conducted to compare

the main competing technologies in the LPWAN

market: LoRa, NB-IoT, and SigFox. Benchmarking is

key to evaluating competing providers, their key

differentials, and their approach to security. The study

conducted by (Pérez, 2022) performed a comparative

study of LPWAN technologies LoRaWAN and

SigFox . While the study conducted by (Stanco, 2022)

on the performance of IoT LPWAN technologies for

NB-IOT, SigFox and LoRaWAN. The results of the

LPWAN benchmarking are presented in Table 8.

Table 6: Illustrating ECB encryption weakness.

ECB Mode – (LoRa-Is-a-LPWAN!)

1 2 3 4

Transmission Number Plaintext Payload Number Of Bytes Ciphertext Payload

1 A 16 9E72427DF27DD22A355C3FB13A3AB1A9

2 A 16 9E72427DF27DD22A355C3FB13A3AB1A9

3 mp047..!bd260h1 16 13B2805995F211854833169FD140361D

4 mp047..!bd260h1 16 13B2805995F211854833169FD140361D

Table 7: Illustrating CBC encryption strength.

CBC Mode – (LoRa-Is-a-LPWAN!)

1 2 3 4

Transmission Number Plaintext Payload Number of Bytes Ciphertext Payload

1 A 16 ceeec1b5d2ec3241f2d53fe07a759257

2 A 16 169e6d845226a055477d6c526ac722f5

3 mp047..!bd260h1 32 e6dac1f02e7df0457d4a245a3decac0a

4 mp047..!bd260h1 32 13dae2ac9c4653a3190de80a3d48ff00

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

Table 8: Illustrating CBC-CMAC encryption strength.

CBC-CMAC Mode – (LoRa-Is-a-LPWAN!)

1 2 3 4

Transmission Number Plaintext Payload Number of Bytes Ciphertext Payload

1 A 32 707154bb1243dce5dbc33a3eaa59fac8

2 A 32 4c8d0b498b4b4264dc8d1c9aa59abf3a

3 mp047..!bd260h1 32 de74920f3f4e1409fb9ba1b01fb6d454

4 mp047..!bd260h1 32 cc684825756423b8c56d3289f3928836

Starting with NB-IoT, it is built upon the Long-

Term Evolution (LTE) wireless standard, which is a

trusted and reliable standard for wireless

communication of mobile devices and data terminals,

hinging on GSM/EDGE technology. Mobile

operators implement a layered model of security in

NB-IoT, encompassing high encryption standards

with AES 256 for communications and data storage.

While its high infrastructure cost could be a barrier

for some applications, its robust security measures

and high payload capacity of 1600 bytes position it

well for applications requiring

secure

and

extensive

data

transmission.

SigFox offers an alternative approach with a

unique ecosystem design where encryption is not

implemented by default. This structure, along with its

maximum payload limit of 12 bytes per message and

140 messages per day, may limit its applicability for

high data transmission applications but proves cost-

effective and efficient for use-cases accepting low

data rates.

LoRa stands out with its proprietary standard

operating primarily on the ISM band, offering

deployment flexibility. A salient feature is its Chirp

Spread Spectrum modulation that helps mitigate

multipath propagation effects for more accurate

transmission. While the LoRaWAN protocol ensures

end-to-end AES 128 encryption, the LoRa physical

layer (LoRa PHY) itself does not inherently include

encryption. The lack of inherent encryption in LoRa

PHY does raise security concerns as seen in this paper;

however, it also lowers infrastructure costs due to its

capacity for point- to-point communication, thereby

eliminating the need for a gateway, unlike

LoRaWAN. This trade-off is an important

consideration for LoRa's adoption in IoT

applications.

Each of the competing LPWAN technologies,

LoRa, NB- IoT and SigFox present distinct strengths

and potential limitations depending on the specific

requirements of the use- case. For LoRa, it has a unique

potential for low-cost, high- coverage range, and more

accurate transmission owing to its modulation scheme.

Table 9: LPWAN Technologies Benchmarking.

LoRa NB-IoT SigFox

Standard

Proprietary Open Proprietary

Spectrum

ISM Licensed ISM

Modulation

Chirp Spread

Spectrum

Orthogonal

Frequency-Division

Multiplexing

D-BPSK

Range

< 20km < 10km >40km

Payload

243 bytes 1600 bytes 12 bytes

Encryption

LoRa PHY: No

inherent

encryption,

LoRaWAN:

AES 128

end-to-end

+AES 256

ransmission

AES 128

Battery

+10 years +10 years +10 years

Cost Low High Low

a. Differential Binary Phase Shift Keying

8 CONCLUSION

In our comprehensive study on LoRa P2P

communication, particularly within the context of

remote smart grids. We have focused on the critical

aspect of payload confidentiality of a LoRa P2P

network, exploring the potential of IoT hardware

encryption features and the application of user-

controlled AES algorithms on an ESP32 device. Our

findings have culminated in a proposal for a robust

solution that leverages AES cryptography in providing

a more secure communication for LoRa P2P

networks.

The experiments we conducted involved

capturing and demodulating LoRa transmission

signals, followed by a thorough analysis of the

payload. We also assessed various encryption

mechanisms and algorithms, leading us to develop

encryption solutions at the user level using the AES

algorithms. These solutions aim to ensure data

confidentiality on ESP32 LoRa devices.

Analysis of Payload Conﬁdentiality for the IoT/ LPWAN Technology ‘Lora’

Our findings indicate that integrating end-to-end

payload confidentiality in LoRa P2P communication

is indeed feasible, although it may involve minor

performance trade- offs. They reveal that the

enhanced security considerably outweighs the minor

performance degradation and a slight increase in

battery usage. This research contributes significantly

to the field of secure communication in remote smart

grids, offering valuable insights into the potential

security risks and trade-offs involved in

implementing LoRa P2P in IoT applications such as

remote smart grids.

In conclusion, our study has highlighted some key

avenues for future investigations. Expanding the

scope of encryption algorithms tested, specifically

venturing into higher-strength solutions such as RSA

and AES-256, focusing on finding the most effective

and power-efficient algorithm offers promising new

research. Furthermore, the investigation of secure

LoRa P2P communication in challenging real-world

scenarios, like in a P2P production environment,

stands out as an important future endeavor,

accentuating a long-term, empirical evaluation of

remote grid performance. We also advocate for

ongoing improvements in LoRa P2P system security,

potentially presenting a cost- efficient alternative to

LoRaWAN for point-to-point communication

infrastructure needs.

Our research expansion has vital implications for

secure IoT applications and aids in developing cyber-

resilient energy infrastructures.

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