ArticlePDF Available

On-Farm Data Security: Practical Recommendations for Securing Farm Data

Frontiers
Frontiers in Sustainable Food Systems
Authors:

Abstract and Figures

The growth in the use of Information and Communications Technology (ICT) and Artificial intelligence (AI) has improved the productivity and efficiency of modern agriculture, which is commonly referred to as precision farming. Precision farming solutions are dependent on collecting a large amount of data from farms. Despite the many advantages of precision farming, security threats are a major challenge that is continuously on the rise and can harm various stakeholders in the agricultural system. These security issues may result in security breaches that could lead to unauthorized access to farmers' confidential data, identity theft, reputation loss, financial loss, or disruption to the food supply chain. Security breaches can occur because of an intentional or unintentional actions or incidents. Research suggests that humans play a key role in causing security breaches due to errors or system vulnerabilities. Farming is no different from other sectors. There is a growing need to protect data and IT assets on farms by raising awareness, promoting security best practices and standards, and embedding security practices into the systems. This paper provides recommendations for farmers on how they can mitigate potential security threats in precision farming. These recommendations are categorized into human-centric solutions, technology-based solutions, and physical aspect solutions. The paper also provides recommendations for Agriculture Technology Providers (ATPs) on best practices that can mitigate security risks.
Content may be subject to copyright.
POLICY AND PRACTICE REVIEWS
published: 21 June 2022
doi: 10.3389/fsufs.2022.884187
Frontiers in Sustainable Food Systems | www.frontiersin.org 1June 2022 | Volume 6 | Article 884187
Edited by:
Shambhu J. Upadhyaya,
University at Buffalo, United States
Reviewed by:
Ting Xie,
University at Buffalo, United States
Sumita Mishra,
Rochester Institute of Technology,
United States
George Lawrence Sanders,
University at Buffalo, United States
*Correspondence:
Rozita Dara
drozita@uoguelph.ca
Specialty section:
This article was submitted to
Climate-Smart Food Systems,
a section of the journal
Frontiers in Sustainable Food Systems
Received: 21 March 2022
Accepted: 26 May 2022
Published: 21 June 2022
Citation:
Hazrati M, Dara R and Kaur J (2022)
On-Farm Data Security: Practical
Recommendations for Securing Farm
Data.
Front. Sustain. Food Syst. 6:884187.
doi: 10.3389/fsufs.2022.884187
On-Farm Data Security: Practical
Recommendations for Securing
Farm Data
Mehdi Hazrati, Rozita Dara*and Jasmin Kaur
School of Computer Science, University of Guelph, Guelph, ON, Canada
The growth in the use of Information and Communications Technology (ICT) and Artificial
intelligence (AI) has improved the productivity and efficiency of modern agriculture, which
is commonly referred to as precision farming. Precision farming solutions are dependent
on collecting a large amount of data from farms. Despite the many advantages of
precision farming, security threats are a major challenge that is continuously on the rise
and can harm various stakeholders in the agricultural system. These security issues
may result in security breaches that could lead to unauthorized access to farmers’
confidential data, identity theft, reputation loss, financial loss, or disruption to the food
supply chain. Security breaches can occur because of an intentional or unintentional
actions or incidents. Research suggests that humans play a key role in causing security
breaches due to errors or system vulnerabilities. Farming is no different from other
sectors. There is a growing need to protect data and IT assets on farms by raising
awareness, promoting security best practices and standards, and embedding security
practices into the systems. This paper provides recommendations for farmers on how
they can mitigate potential security threats in precision farming. These recommendations
are categorized into human-centric solutions, technology-based solutions, and physical
aspect solutions. The paper also provides recommendations for Agriculture Technology
Providers (ATPs) on best practices that can mitigate security risks.
Keywords: information security, security breach, digital agriculture, farming, security standards, securing farms,
Agriculture Technology Providers
INTRODUCTION
With the advancement of Artificial Intelligence (AI) and Internet of Things (IoT), the use of
precision agriculture has been exponentially increasing. According to the MarketsandMarkets
report (MarketsandMarkets, 2022), $8.5 billion USD was the precision farming market in 2022
and is expected to grow to 15.6 billion by 2030. Precision farming uses technological innovations
in data collection and processing to enhance farm production and raising livestock. Precision
agriculture has enabled farmers to make more informed decisions about farm production and
use of resources. For example, with the help of precision agriculture technologies, farmers can
monitor the crop status by observing and measuring variables such as soil condition, irrigation,
fertilizer and pesticide effect, plant health, and crop yield. Similarly, the farmers can monitor the
health of livestock animals by recording their food and water intake, weight, behavior, temperature,
respiration rate, and sounds produced by animals.
Hazrati et al. Farm Data Security Recommendations
These technologies are highly dependent on processing large
volumes of data collected from farms (Cisternas et al., 2020).
The data are collected using smart sensors, agricultural drones,
and other farm technologies. According to IBM, 500,000 data
points were being generated on an average farm per day
in 2019 (IBM, 2021). This size is expected to grow to 4
million by 2036. The collected data helps gain insights into
farm management practices, including tracking crop yield,
environmental sustainability factors, pest risk analysis, livestock
health and welfare monitoring, and even food safety and security.
Precision farming can be compromised by threats to data
acquisition technologies and increased cyberattacks on farming
systems (Threats to Precision Agriculture, 2018). Farming
technologies and access to farm data attract the attention
of cybercriminals who are looking to take advantage of
vulnerabilities in the system for financial gain or other malicious
intents (Amiri-Zarandi et al., 2020). Weaknesses in the system,
fragmented technical security protocols, and human errors can
allow attackers to easily access the farming network and the
digital tools and introduce security breaches and risks. Security
breach refers to unauthorized access and transmission of data
which can result in leakage of sensitive, protected, or confidential
information. Security breaches in the farming system may lead to
compromising the entire system or bringing the network down
allowing the attackers to control the entire system, and sabotage
farm data or other assets. Coordinated attacks on a farming
system can lead to stealing data, loss of proprietary information
(such as contracts or security design), disruption to supply chain,
or even reputation loss and financial loss.
West (2018) shares his view on security in farms and describes
that there are two types of digital farming systems; those
that have been hacked, and those that will be hacked in the
future. Security of farms is hampered by the security attacks
that are being prevalent and targeted in the agricultural sector.
For example, the “REvil group” attacked and compromised
the Dairy Farm Group’s network, one of the largest retailers
in Asia, and demanded roughly $30 million ransom and had
access to the Dairy Farm network (Connor Madsen, 2021).
Similarly, a hacking group, BlackMatter, carried out an attack on
NEW Cooperative, a farmer cooperative, and locked up all the
computer systems. BlackMatter demanded $5.9 million ransom
and threatened to publish a terabyte of the cooperative’s data.
The company was forced to take their computer network offline
to isolate the incursion. JBS, the world’s largest meat-processing
corporation based in Brazil, had to pay $11 million ransom
since their computer network was hacked and the company was
forced to temporarily shut down nine beef plants, disrupting
meat processing across North America and Australia for an
entire week. In 2019, HSBC issued a warning to farmers in the
UK about the risks of cybercrime, such as phishing campaigns,
ransomware attacks, and malware. Such security attacks can
harm the agricultural sector and disrupt countries’ economies
that are highly reliant on agriculture.
Security attacks can result in security breaches which can
have a detrimental effect. Figure 1 provides a classification of
security breaches. Security breaches can be caused intentionally
or unintentionally on a farm system. For example, hacking is
FIGURE 1 | Classification of security breaches.
exploiting weaknesses in a network by a person who intentionally
wants to penetrate the system (Gao and Zhong, 2015). Another
example of an intentional attack is Malware. Malware is a
software product designed to cause disruption in a network or
system or gain unauthorized access to data (Rust et al., 2022).
Ransomware is prevalent malware that attacks industries and
new technologies that work with data. Ransomware can encrypt
the files on a device and prevent users from accessing their
information. The attackers usually display a ransom demand
on the computer screen to release the locked information.
Phishing is also a common cybersecurity scam that can harm
farms’ digital infrastructure (De Araujo Zanella et al., 2020;
Van Der Linden et al., 2020). Through phishing, a hacker
obtains some sensitive information through an email, phone
call, or text message. Phishing can target an individual or
a larger group of recipients and the attacker establishes a
communication that seems to be a regular vendor of a business
or a known contact. On the other hand, unintentional actions
can also lead to many security breaches. Accidental data sharing,
transmitting sensitive data without proper encryption, and
unauthorized access to computing infrastructure resulting from
wrong configurations are examples of unintentional threats.
Unauthorized software installation and configuration errors are
other examples of unintentional actions that may grant access
to sensitive information or computing infrastructure (Cheng
et al., 2017). Best practices and technology solutions that can
protect farm systems from these threats are reviewed in the
following sections.
Humans play a significant role in the security of IT
infrastructure since attackers often use social engineering tactics
to infiltrate or compromise a system. Hughes-Lartey et al.
presented the relationship of security breach incidents and
human factors (Hughes-Lartey et al., 2021). Human factors that
can lead to security breaches include but are not limited to lack
Frontiers in Sustainable Food Systems | www.frontiersin.org 2June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
of awareness, negligence, or using inappropriate access control
practices. Moreover, studies show that employee negligence and
insider involvement are the weakest links in an organization
that may cause 95% of data breaches. These intentional or
unintentional human errors can pose serious security risks.
As indicated above, farming systems are also vulnerable
to cyber security attacks. Limited cybersecurity awareness
among farmers, outdated security practices, lack of compliance
with security standards, and prioritizing productivity on
farms/farmers over the security of farm data are some of
the reasons that can increase security attacks. Some research
studies have also shown that there is a lack of emphasis on
cybersecurity in the farming industry. Nikander et al. (2020)
investigated the network and connected devices of six dairy farms
in Finland and also conducted a survey of security practices
at farms. The authors concluded that there is a significant
need to improve farm system security at individual farm levels.
They have also concluded that security threats are caused by
farmers’ activities or lack of activities, i.e., practicing security
protection standards. Farm physical environment imposes its
security challenges according to this study. Farmers in this
survey have also expressed a lack of confidence in their ability
to protect their farms against such attacks (Nikander et al.,
2020). These findings suggest that enhancing farmers’ awareness
about security best practices can assist in safeguarding farm data
and systems.
This paper provides a series of recommendations for farmers
to improve security of farm IT infrastructure and farm data.
We categorize our recommendations in three groups including
human centered, technology-based and physical security for
farmers. Furthermore, farm data cannot be effectively protected
without reliable security technologies. In this paper, we elaborate
on the important role ATPs or companies that build or govern
farming systems and platforms can play to protect farm data.
We also briefly review some of the security best practices and
technologies that ATPs can provide or recommend to farmers.
These recommendations can assist in enhancing system security
and mitigating possible security risks and attacks.
SECURITY PRACTICES RECOMMENDED
FOR FARMERS
Information security is an important requirement that farmers
should pay careful attention to. Farmers should adopt security
best practices to protect farm systems from potential cyber
attacks. In general, the source of security issues can be divided
into three categories: human error, technology, and physical
aspects. Human errors refer to unintentional actions or lack of
actions by users that allow a security breach to occur. Examples
of such actions include downloading an email attachment
containing malware, opening a phishing email, using weak
passwords, and sharing personal passwords with others. As per
IBM’s report (IBM Security Services, 2014), human error is the
root cause of 95% of security breaches. Technological security
risks, such as ransomware and malware, are risks that are
caused by software vulnerabilities, deficiencies in system design
FIGURE 2 | The main categories of security issues.
or setups, configuration errors, or other technology-related issues
(e.g., lack of interoperability). Finally, physical data security
issues are anything tangible that causes a security breach, such
as unauthorized access to server rooms. Each of these security
categories can cause irreparable damage to the farming system.
Figure 2 demonstrates these main causes of most security threats.
In this section, we provide recommendations on security
practices that can be adopted by farmers and farmworkers for
each of the threat categories in Figure 2.
Human-Centered Solutions
Farmers as the end-users of farming technologies can be
instrumental in securing farm IT infrastructure. Farmers and
farmworkers’ lack of awareness about potential security risks and
practices may put farm technologies in a compromising security
situation (Nikander et al., 2020). Several steps can be taken to
tackle the human aspects of cybersecurity at farms which are
imposed by the end-users. This section reviews these solutions.
Farmers should educate themselves and all farmworkers to
improve their information security competency and to have a
reasonably good level of awareness and vigilance. Furthermore,
farmworkers should be trained to adopt day-to-day cybersecurity
hygiene and best practices to take proactive steps in protecting
farms’ digital infrastructures such as servers, sensors, data, and
other digital devices. For example, farmworkers should be trained
and informed not to download apps from unknown or unreliable
sources. They should also be aware of the harmful consequences
of phishing emails. This can be achieved by showing examples
of phishing emails and presenting the impacts of those emails.
Farmworkers should be encouraged to report suspicious system
performances to get help before other parts of the system are
impacted (Hanus and Wu, 2016).
The common process to access a digital system is by using
an authorized credential such as a username and password.
Verizon states that the use of compromised or stolen credentials
caused over 60% of security breaches in 2021 (Barr, 2021). This
shows the importance of credential management practices such
as password management and two-factor authentications. To
prevent farm data from getting breached, farmers should work
with ATPs to implement appropriate credential management and
user authentication practices, e.g., passwordless or password-
based practices. Through these practices, authorized users can
be identified by their passwords or biometrics to prevent
unauthorized access (Butler and Butler, 2014). Hackers can
exploit weak, short, or exposed passwords to break into a system.
If the system’s authentication process is through passwords, using
Frontiers in Sustainable Food Systems | www.frontiersin.org 3June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
TABLE 1 | Guidelines for creating strong passwords.
Guidelines to create and use strong passwords for authentication
1. Create a password longer than the recommended minimum required length
by the system.
2. Use a combination of characters to create a password, including small and
capital letters, numbers, and symbols.
3. Avoid choosing frequently-used words and personal information that are
easy to guess (such as first/last name, pet name, or family member’s name).
4. To remember the password, a random sentence and abbreviations can be
used. Complexity can be added by capitalizing certain letters, such as
“Take-Me2-Home!”.
5. In case there is a need to write the passwords so that they are not forgotten,
it is recommended to put the notes in a safe place.
6. Create separate usernames or passwords for different farm staff if possible.
7. Usernames or passwords should not be shared with others.
8. Change passwords periodically to alleviate the risk of compromise.
9. Do not save passwords or use auto-fill features on browsers.
10. If the system is compromised or it has been hacked, passwords should be
changed immediately.
11. Change the default passwords of devices and machines at the farm while
setting them up for the first time.
12. Use a random password generator or password management system if
possible.
reliable passwords is one of the most effective precautions to
keep access to the farm’s digital infrastructure safe for authorized
users. This can be achieved by defining strong passwords. Table 1
contains recommendations for farmers and farmworkers to
create strong passwords and to keep passwords secure.
Malware can impose different kinds of vulnerabilities on the
digital systems installed at the farm. Malware is a program
designed to gain unauthorized access to a network or system to
steal information or cause damage to data or software. Malware
can be distributed/downloaded by clicking on a link in an
email, through a malicious website, or by executing a malicious
file. Ransomware is one of the prevalent malware that can
impact various sectors, including farming (West, 2018). One
way of preventing malware such as ransomware, farmworkers
should avoid opening suspicious emails, links, and files and
should not connect to untrusted memory devices or computers.
They should also avoid downloading software applications from
unknown or untrusted sources on farm computing devices.
Farmworkers should avoid signing in with their personal
account on applications and websites online. They should also
avoid linking online accounts to farm desktops, computers, or
networks. Once an attacker gains access to an account associated
with a device or network at the farm, they can use this channel to
connect to other accounts or systems connected to a farm.
Phishing attacks are other prevalent threats in the farming
ecosystem (Carneiro et al., 2021). Farmworkers should always
be vigilant about potential social engineering traps such as
phishing attacks, spam, and social media scams. Training
farmworkers with practical examples by presenting past incidents
and their consequences is a pragmatic way to raise awareness
and prepare the staff to identify, avoid, or mitigate such attacks
(Carneiro et al., 2021). It is also recommended not to click
on links or open attachments embedded in suspicious emails.
Farmworkers should further avoid sharing sensitive information
with unknown/untrusted organizations or contacts. The source
and purpose of information sharing should be clear and should
be discussed with farm managers or ATPs.
Although some confidential data leakage incidents in a farm
can be due to farmworkers’ negligence or lack of action, in some
cases, a data breach may take place intentionally by farm staff.
Such incidents are referred to as insider attacks (Bae et al., 2011).
In this type of attack, a disgruntled staff can sell confidential data
to make a profit or disseminate data to cause harm. Proactive
prevention of such attacks is crucial because the insider attackers
have legitimate access to the system. Limiting those who have
administrative access to the network, i.e., access privilege, and
limiting the farmworkers’ access to unrelated components of the
farming system (Yang et al., 2020) that are not related to their
work (e.g., data, devices, servers) can alleviate the risk of insider
attacks. Access privilege can be set up by the ATPs through
consultation with farm managers. For example, if a farm staff
requires elevated, privileged access to the system, setting up a
custom username-password and limiting permissions on what
they can do can prevent unauthorized activities. Additionally,
it is necessary to grant minimum access for farmworkers and
only at a level that they can perform their job, which is also
called zero trust (Campbell, 2020). If the work contract of a farm
staff terminates and they are no longer associated with the farm
operations, it is important to immediately revoke their access to
farm computers, servers, and systems and change passwords even
for emails (Data Security Policy, 2021). Also, if the farm staff has
other work devices, such as a laptop or USB drive, those devices
should be returned before quitting the job and remote access or
other access privileges such as passwords should be deactivated
to prevent unauthorized access to farm system.
In the case of an attack on the farm system and infrastructure,
the passage of time can only worsen the situation. Therefore, it
is highly recommended that farmers mitigate harm such as data
leakage, system crashes, and compromised device or computer
by seeking help from ATPs or farm insurances in the shortest
amount of time possible. A response plan should be provided by
ATPs for the farmers to execute the appropriate steps and detect
and limit the damage and facilitate a quick recovery (Thompson,
2018). The plan should leverage all significant scenarios that
could occur on farms. Also, it is suggested to prepare an on-call
list of technology providers, technicians, and farmworkers who
can respond to security events.
Technological Solutions
On-farm data acquisition technologies and other smart
technologies can be susceptible to security risks. For this reason,
appropriate technical measures should be taken to reduce
vulnerabilities and secure the technological aspects of farms
(Hamed et al., 2017). This section provides recommendations to
farmers on the technology solutions they can obtain to secure
data at farms.
A number of solutions can be implemented by the ATPs to
protect farm computing infrastructure from such threats. For
Frontiers in Sustainable Food Systems | www.frontiersin.org 4June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
instance, it is often the case that some farm operations are
performed remotely. To do this, farmworkers need to connect
to the farm network remotely to monitor, control, or perform a
task with their own personal device. Setting up the heating system
of the farm or turning off the irrigation system remotely while
raining are examples of such remote activities. Personal devices
are inherently insecure and may have unpatched vulnerabilities
(Data Security Policy, 2021). In those cases, farmers can request
access to farm computing infrastructure through Virtual Private
Network (VPN). Farmworkers can use VPN to connect remotely
to perform farm operations securely. Also, connecting from
unsecured Wi-Fi networks such as open or public Wi-Fi can
enable attackers to capture traffic off an open access point
to perform attacks. Setting up multi-factor authentication can
mitigate such security issues (Yang et al., 2020). Multi-factor
authentication performs an additional sign-in method, such
as sending a text or a number to your phone or email with
a code to confirm the identity. Some authentication methods
use a face ID (Fard and Hashemi, 2020) or fingerprint as an
extra step after entering a username and password. The extra
step to log in to the device or system can help protect the
farming system from getting hacked or from unauthorized access.
Protection from malware and ransomware can be improved by
using antivirus software on all computers and laptops that are
used at the farm. Furthermore, on-farm software devices need to
be updated frequently. Automated updates can be set up to make
the process easier.
IoT devices have different levels of security standards which
are mentioned in device specifications (Matheu-García et al.,
2019). Examples of digital farm devices include sensors, IoT
irrigation systems, milking robots, and drones. Using devices
with weak security levels, such as devices using unencrypted
passwords, can increase the vulnerabilities of farm systems and
networks. To mitigate this issue, it is recommended that farmers
ask the technology providers about the farm devices’ security
features before they are installed at the farm. Farmers can also
ask ATPs to enable the use of encryption for all data at rest
or data in motion. It is recommended to list all equipment,
hardware, and software available in the farm system and update
the list when any new equipment is purchased. This list can help
farmworkers to ensure all software and IoT devices firmware is
up to date. Also, farmworkers can check the list periodically to
find and uninstall unused software or devices (Matheu-García
et al., 2019). Eliminating unused software and hardware and
keeping available ones up to date can increase the security level
of the entire agricultural system. Finally, if it is suspected that the
system is infected with malware, it is recommended to disconnect
the computer or device from the network to prevent the malware
from spreading. The system should be scanned with an antivirus
program. It is also recommended to ask ATPs to reinstall the
system as the attacker can use some other techniques to access
the compromised system.
Data loss in a farming system is a challenging issue that should
be proactively addressed. Data loss can result from hardware
or software problems, attacks on data, or accidental deletion
of data. For example, malware and ransomware can lead to
intentional data corruption and hardware or software problems
(such as improper encryption) that can lead to unintentional data
corruption (West, 2018). Backing up data regularly in the cloud
and or other storage mediums off-farm is an easy approach to
ensure that any issues with the data at farm do not impact the
data backups (Thomas and Galligher, 2018). This strategy allows
data to be restored from backup copies from an earlier point
in time. Most of the recommendations provided in this section
focus on adopting existing security standards and best practices
by farmers to enhance security. To implement these or other
technical solutions, farmers can ask the ATPs to install or activate
these solutions or may recruit a trusted technician/consultant or
seek technology providers’ assistance to enhance data security in
the farm system.
Physical Solutions
Issues arising from physical threats can also harm the farm
system. This is a major threat for many farms given their open
environment. The following are recommendations to secure
physical space on the farm.
First of all, the farm entrance should be protected through
digital locks or other means. It is also recommended that
unauthorized entry should be strictly prohibited. Farmworkers
should also be aware if they are expecting an outsider. For
example, repair workers or maintenance workers should make
prior arrangements for their visit to the farm. Farmworkers
should not allow anyone to enter the premises without verifying
their identity or purpose of visit. This can help in protecting
farms from trespassing and access to main IT infrastructure,
servers, routers, and other assets.
To monitor the physical barriers of the farm, surveillance
equipment such as a security camera system, motion detector,
and door alarms should be installed. It is also important to
protect sensitive information, documents, and devices by placing
them in secure spaces, such as locked cabinets, rooms, and off-
site caches. Unnecessary copies of personal or sensitive data
raise the risk of disclosure; therefore, they should be wiped
or shredded. Moreover, physical access control and locking
classified places can mitigate the risk of unauthorized access to
devices and information.
Another physical security requirement is to protect electronic
devices from failure. For example, since electronic devices and
computers are vulnerable to dust, they need to be kept clean.
This requires regular cleaning services of the electronic devices
to decrease the risk of failure. Providing stable power as required
for IoT devices and computers and installing regulators to reduce
voltage fluctuation are other actions that can be taken to alleviate
the risk of device failure.
THE ROLE OF AGRICULTURAL
TECHNOLOGY PROVIDERS
Since ATPs design and deploy the technologies in farms, they
can be instrumental in preventing potential security attacks and
risks. Also, it is common that technology providers have access
to digital infrastructure and information at the farm, such as
farm networks, sensors, or data sources stored on-farm or in the
Frontiers in Sustainable Food Systems | www.frontiersin.org 5June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
TABLE 2 | Practices that are recommended to ATPs.
Security practices recommended to Agricultural Technology Providers
(ATPs)
1. Encrypt data transfer and data stored on devices and servers.
2. Keep operating systems and all software up to date to enable software
patching.
3. Install and update virus scanners and malware detection software regularly.
4. Use firewalls on all computers and the entire network to protect against
attacks. Ensure routers and firewalls are appropriately set up, configured and
up to date.
5. Perform continuous vulnerability assessment, penetration test, and
end-to-end monitoring in the entire farm system to monitor and address the
shortcomings in advance (Threats to Precision Agriculture, 2018).
6. Install antivirus software to secure farm systems as well employee-owned
devices (routers, phones, tablets, workstations, and servers) (Threats to
Precision Agriculture, 2018).
7. Ensure controlled use of administrative privileges to prevent unauthorized
access to sensitive data.
8. Utilize user authentication mechanisms, e.g., multi-factor authentication
(Ometov et al., 2018), to validate user identity when accessing common cloud
services.
9. Enable “privileged access” control to farm data to protect sensitive
information and ease access to less sensitive information that are used
frequently for farm management.
10. Ensure the Wi-Fi connection is private and regularly monitor it to prevent
attackers from accessing sensitive data. Remote access through VPN can also
be enabled.
11. Use secure channel and communication protocols for all connections and
data transfers.
12. Use automatic session timeout and configuration to automatically log out
after a defined time to decrease the risk of illegal access to farm system
(Carneiro et al., 2021).
13. Provide data recovery capabilities as well as a reliable backup system.
14. Use de-identification at source techniques from the first steps of data
collection to protect farmers’ privacy (Zaman et al., 2016).
15. Prepare incident response and disaster recovery plans to manage potential
risks in the system.
16. Use emerging technologies such as blockchain or passwordless
authentication systems.
cloud. This means that working with a trusted ATP is critical in
providing the required security level in the farm system.
Adopting information security standards by ATPs in precision
farming systems is important for implementing a highly secure
environment (Threats to Precision Agriculture, 2018). Security
standards and practices should be embedded as a default feature
in the digital IT architecture and design (Vallois et al., 2019).
Security by design recommends a set of standards that can be
used in all the application development (or installation) stages,
from requirement analysis, implementation, and all the way
to production. User-friendliness of these features is important
to encourage use and adoption by farmers and farmworkers
as they will be more intuitive to learn and remember. For
example, using a secure, easy to use, and effective biometric or
password management system for logging in instead of using
traditional passwords can simplify access to systems in a secure
manner and reduce the possibility of unauthorized access or
errors (Obaidat et al., 2019). Also, designing a logout button in
applications to remind farm staff to log out of the system while
leaving or an automatic log out function can assist with locking
the system when they are not in use by authorized staff.
The software on the farm devices should be kept up to date to
protect from malware and ransomware. Since having up-to-date
software decreases the risk of security threats, it would be best to
set the computers, tablets, cell phones, and other digital devices to
update software automatically. ATPs should provide farmers with
instructions on how to install and update security software tools.
It is also essential to install reliable firewalls on the systems and
entire network to keep them up to date all the time (Cain et al.,
2018). A firewall is a network security software or device that can
monitor incoming and outgoing traffic to the network and filter
suspicious activities based on security rules and machine learning
based detectors.
ATPs can use security enhancing tools on the farm system
or network to detect insider attacks and inform farmers about
possible threats proactively. Enabling software tools that can
monitor network activity from time to time and checking for
suspicious activities are important to secure the farm system
and network. For example, access to a farm system after work
hours can be an indication of illegitimate behavior. Also, ATPs
can use a location-based key management method to combat
insider threats (Choi et al., 2015). This method automatically
monitors the location of the person who tries to access the system
remotely. In the case of suspicious or unknown locations, it asks
for stronger authorization methods to ensure legitimate access to
the system (Alneyadi et al., 2016).
After designing the farm system, ATPs can mitigate security
risks in the farm by performing vulnerability assessments
and penetration testing. Vulnerability assessment can identify
vulnerabilities in the system and create a set of recommendations
to fix them (Alhazmi and Malaiya, 2005). This identification can
help prevent risks to the entire network and infrastructures of
a digital farming environment that may impact farm operations
and processes. Furthermore, penetration testing simulates an
attack to assess the security level of a system (Bacudio et al., 2011).
This complementary testing mechanism helps farm staff and the
entire system to be prepared for potential cybersecurity issues
in advance. In addition, using proper encryption for all data
transfers and data stored at farm servers is a secure mechanism
to protect from security breaches and risks.
With the rise of emerging technologies such as blockchain
and passwordless authentication systems, ATPs can consider
these technology solutions to protect farm data. Blockchain
is a distributed database and immutable ledger that enables
secure transactions and transfer of ownership. It also ensures
transparency and trust among stakeholders (Xiong et al., 2020).
Blockchain can ensure privacy of transactions, ownership rights,
and provide greater control of data for farmers by enabling
an effective mechanism for identity management. Passwordless
authentication mechanism is an effective way to validate identity
of the user and allow secure access to computing infrastructure
at the farm. They have shown to be more user-friendly than
passwords and can improve compatibility (e.g., interoperability)
(Parmar et al., 2022).
Frontiers in Sustainable Food Systems | www.frontiersin.org 6June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
Table 2 provides recommendations on security practices that
ATPs can adopt to secure farm data and digital infrastructure.
CONCLUSION
Security breaches can cause irreparable harm to farmers. The
consequences of security threats include, but are not limited to,
stealing information, reputation loss, destruction of equipment,
error in system configuration and performance, and gaining
an improper financial advantage over a competitor. Existing
research suggests that 95% of security breaches are caused by
humans. In addition, past research by Hanus and Wu (2016) has
shown that farmers are not aware of security practices that can be
used to protect their farms.
This gap suggests the need for security standards that can
be applied to protect farms. Connectivity of farm systems
with other systems such as smart cities, supply chain, and
other smart facilities enforces the necessity for securing digital
infrastructures at farms. In addition, many countries have
considered agriculture as the critical national infrastructure that
requires extra protection. The objective of this paper is to
provide recommendations on the adoption of existing security
best practices by farmers and ATPs to prevent security breaches
and sensitive data leakage. For the farmers, recommendations
were provided in three categories of human errors, physical
threats, and technology threats. ATPs can secure the farm digital
infrastructure by adopting standards for securing technology,
process, and protocols. The role of governments is also
important to enforce policies and principles related to farm
data security. As for the future direction, we will work with
the national farm associations to present this research in a
usable format for farmers. We expect that farmers’ trust and
long-lasting relationship with these associations will enhance an
adoption of the recommended security practices (Rust et al.,
2022).
AUTHOR CONTRIBUTIONS
MH and RD contributed to conception of the study. All authors
have contributed to writing the paper, manuscript revision, read,
and approved the submitted version.
FUNDING
This research was funded by a Natural Sciences and Engineering
Research Council of Canada (NSERC) Discovery Grant and
Ontario Ministry of Agriculture Food and Rural Affairs, New
Directions, funding awarded to RD.
REFERENCES
Alhazmi, O. H., and Malaiya, Y. K. (2005). “Quantitative vulnerability assessment
of systems software, in Annual Reliability and Maintainability Symposium,
2005. Proceedings, 615–620.
Alneyadi, S., Sithirasenan, E., and Muthukkumarasamy, V. (2016). A survey
on data leakage prevention systems. J. Netw. Comput. Appl. 62, 137–152.
doi: 10.1016/j.jnca.2016.01.008
Amiri-Zarandi, M., Dara, R. A., and Fraser, E. (2020). A survey of machine
learning-based solutions to protect privacy in the Internet of Things. Comput.
Sec. 96, 101921. doi: 10.1016/j.cose.2020.101921
Bacudio, A. G., Yuan, X., Chu, B.-T. B., and Jones, M. (2011). An overview of
penetration testing. Int. J. Netw. Sec. Appl. 3, 19. doi: 10.5121/ijnsa.2011.3602
Bae, K., Kim, S., Lee, Y., You, I., Yim, K., and Son, T. (2011). “Insider threats are
getting worse within industries: isolated secondary backup required, in 2011
Third International Conference on Intelligent Networking and Collaborative
Systems 652–655. doi: 10.1109/INCoS.2011.165
Barr, B. (2021). Data Breach Investigations Report. Credential Stuffing, Data
Breaches, Malware. Avaialbe online at: https://spycloud.com/highlights-
from-the- verizon-2021- data-breach- investigations-report/#:$\sim$:text=
Criminals Want Credentials,cause of all data breaches.andtext=According to
Verizon%2C 61%25 of,25%25 of breaches last year (accessed January 28, 2021).
Butler, R., and Butler, M. (2014). “An assessment of the human factors
affecting the password performance of South African online consumers, in
HAISA, 150–161.
Cain, A. A., Edwards, M. E., and Still, J. D. (2018). An exploratory study
of cyber hygiene behaviors and knowledge. J. Inf. Sec. Appl. 42, 36–45.
doi: 10.1016/j.jisa.2018.08.002
Campbell, M. (2020). Beyond zero trust: trust is a vulnerability. Computer 53,
110–113. doi: 10.1109/MC.2020.3011081
Carneiro, R., Duncan, S., Ramsey, F., Seyyedhasani, H., and Murch, R. (2021).
Cyber Attacks in Agriculture: Protecting Your Farm and Small Business With
Cyberbiosecurity. Virgina: VCE Publications.
Cheng, L., Liu, F., and Yao, D. (2017). Enterprise data breach: causes, challenges,
prevention, and future directions. Wiley Interdiscip. Rev.: Data Min. Knowl.
Discov. 7, e1211. doi: 10.1002/widm.1211
Choi, J., Bang, J., Kim, L., Ahn, M., and Kwon, T. (2015). Location-based key
management strong against insider threats in wireless sensor networks. IEEE
Syst. J. 11, 494–502. doi: 10.1109/JSYST.2015.2422736
Cisternas, I., Velásquez, I., Caro, A., and Rodriguez, A. (2020). Systematic literature
review of implementations of precision agriculture. Comput. Electron. Agric.
176, 105626. doi: 10.1016/j.compag.2020.105626
Connor Madsen (2021). Cyber News Rundown: Dairy Farm Ransomware. Available
online at: https://www.webroot.com/blog/2021/02/03/cyber-news-rundown-
dairy-farm- ransomware/ (accessed December 10, 2021).
Data Security Policy (2021). Risk Management Practice Guide of Lawyers Mutual.
Avaialbe online at: https://nmcdn.io/e186d21f8c7946a19faed23c3da2f0da/
556712d9bf0f4cb2a916cc810687d52b/files/risk-management- resources/
practice-guides/Data_Security_Policy.pdf (accessed December 15, 2021).
De Araujo Zanella, A. R., da Silva, E., and Albini, L. C. P. (2020). Security challenges
to smart agriculture: current state, key issues, and future directions. Array 8,
100048. doi: 10.1016/j.array.2020.100048
Fard, S. M. H., and Hashemi, S. (2020). Proposing a sparse representational based
face verification system to run in a shortage of memory. Multimed. Tools Appl.
79, 2965–2985. doi: 10.1007/s11042-019-08491-3
Gao, X., and Zhong, W. (2015). Information security investment for competitive
firms with hacker behavior and security requirements. Ann. Oper. Res. 235,
277–300. doi: 10.1007/s10479-015-1925-2
Hamed, T., Dara, R., and Kremer, S. C. (2017). “Intrusion detection in
contemporary environments, in Computer and Information Security
Handbook, ed J. R. Vacca (Burlington, MA: Elsevier), 109–130.
doi: 10.1016/B978-0-12-803843-7.00006-5
Hanus, B., and Wu, Y. (2016). Impact of users’ security awareness on desktop
security behavior: A protection motivation theory perspective. Inf. Syst. Manag.
33, 2–16. doi: 10.1080/10580530.2015.1117842
Hughes-Lartey, K., Li, M., Botchey, F. E., and Qin, Z. (2021). Human factor,
a critical weak point in the information security of an organization’s
Internet of things. Heliyon 7, e06522. doi: 10.1016/j.heliyon.2021.
e06522
IBM (2021). IBM AI and Cloud Technology Helps Agriculture Industry Improve
the World’s Food and Crop Supply. IBM. Avaialbe online at: https://newsroom.
ibm.com/2019-05- 22-IBM- AI-and- Cloud-Technology- Helps-Agriculture-
Frontiers in Sustainable Food Systems | www.frontiersin.org 7June 2022 | Volume 6 | Article 884187
Hazrati et al. Farm Data Security Recommendations
Industry-Improve- the-Worlds- Food-and- Crop-Supply (accessed January 8,
2021).
IBM Security Services 2014 Cyber Security Intelligence Index. (2014). Analysis
of Cyber Attack and Incident Data from IBM’s Worldwide Security
Operations. Armonk, NY: IBM Global Technology Services. Available
online at: https://i.crn.com/sites/default/files/ckfinderimages/userfiles/images/
crn/custom/IBMSecurityServices2014.PDF
MarketsandMarkets (2022). Avaialbe online at: https://www.marketsandmarkets.
com/Market-Reports/precision- farming-market-1243.html (accessed May 20,
2022).
Matheu-García, S. N., Hernández-Ramos, J. L., Skarmeta, A. F., and Baldini, G.
(2019). Risk-based automated assessment and testing for the cybersecurity
certification and labelling of IoT devices. Comput. Stand. Interfaces 62, 64–83.
doi: 10.1016/j.csi.2018.08.003
Nikander, J., Manninen, O., and Laajalahti, M. (2020). Requirements for
cybersecurity in agricultural communication networks. Comput. Electron.
Agric. 179, 105776. doi: 10.1016/j.compag.2020.105776
Obaidat, M. S., Rana, S. P., Maitra, T., Giri, D., and Dutta, S. (2019). “Biometric
security and internet of things (IoT), in Biometric-Based Physical and
Cybersecurity Systems, ed M. S. Obaidat, I. Traore, I. Woungang (Cham:
Springer), 477–509. doi: 10.1007/978-3-319-98734-7_19
Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T., and
Koucheryavy, Y. (2018). Multi-factor authentication: a survey. Cryptography 2,
1. doi: 10.3390/cryptography2010001
Parmar, V., Sanghvi, H. A., Patel, R. H., and Pandya, A. S. (2022). “A
comprehensive study on passwordless authentication, in International
Conference on Sustainable Computing and Data Communication Systems
(ICSCDS). doi: 10.1109/ICSCDS53736.2022.9760934
Rust, N., Stankovics, P., Jarvis, R. M., Morris-Trainor, Z., de Vries, J. R., Ingram, J.,
et al. (2022). Have farmers had enough of experts? Environ. Manag. 69, 31–44.
doi: 10.1007/s00267-021-01546-y
Thomas, J., and Galligher, G. (2018). Improving backup system evaluations in
information security risk assessments to combat ransomware. Comput. Inf. Sci.
11. doi: 10.5539/cis.v11n1p14
Thompson, E. C. (2018). Cybersecurity Incident Response: How to Contain,
Eradicate, and Recover From Incidents. Apress. doi: 10.1007/978-1-4842-3870-7
Threats to Precision Agriculture (2018). Avaialbe online at: https://www.dhs.gov/
sites/default/files/publications/2018AEP_Threats_to_Precision_Agriculture.
pdf (accessed November 30, 2021).
Vallois, V., Guenane, F., and Mehaoua, A. (2019). “Reference architectures
for security-by-design iot: Comparative study, in 2019 Fifth
Conference on Mobile and Secure Services (MobiSecServ), 1–6.
doi: 10.1109/MOBISECSERV.2019.8686650
Van Der Linden, D., Michalec, O. A., and Zamansky, A. (2020). Cybersecurity
for smart farming: socio-cultural context matters. IEEE Technol. Soc. Mag. 39,
28–35. doi: 10.1109/MTS.2020.3031844
West, J. (2018). A prediction model framework for cyber-attacks to
precision agriculture technologies. J. Agric. Food Inf. 19, 307–330.
doi: 10.1080/10496505.2017.1417859
Xiong, H., Dalhaus, T., Wang, P., and Huang, J. (2020). Blockchain
technology for agriculture: applications and rationale. Front. Blockchain
3:7. doi: 10.3389/fbloc.2020.00007
Yang, X., Shu, L., Chen, J., Ferrag, M. A., Wu, J., Nurellari, E., et al.
(2020). A survey on smart agriculture: development modes, technologies,
and security and privacy challenges. IEEE/CAA J. Automat. Sin. 8, 273–302.
doi: 10.1109/JAS.2020.1003536
Zaman, A. N. K., Obimbo, C., and Dara, R. A. (2016). “A novel differential privacy
approach that enhances classification accuracy, in Proceedings of the Ninth
International CConference on Computer Science and Software Engineering,
79–84. doi: 10.1145/2948992.2949027
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or those of
the publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Copyright © 2022 Hazrati, Dara and Kaur. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is permitted, provided the original
author(s) and the copyright owner(s) are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these terms.
Frontiers in Sustainable Food Systems | www.frontiersin.org 8June 2022 | Volume 6 | Article 884187
... Farmers frequently lack clarity about data ownership and may have limited control over how their information is used or shared, particularly in cross-border collaborations where varying privacy standards complicate compliance.  Mask R-CNN (segmentation and extraction)  GradCAM (visualization and interpretation)  Accuracy tested using Dense-Net121 93.5 [17] Since deep learning-based disease detection relies heavily on these data, ensuring their privacy is of the utmost importance since the potential risks associated with the breach of such data can be detrimental, resulting in various negative outcomes, including the disruption of crop yields, financial crises, compromised farming business strategies, unfair competition, and even threats to food and financial security [18]. Therefore, while CNN-based proposed model Classification of crop diseases intelligence (AI) in agriculture, particularly for disease detection in crops, has made significant strides, as evidenced by various studies (Table 1) employing methodologies like convolutional neural networks (CNNs) and spectral analysis. ...
... Farmers frequently lack clarity about data ownership and may have limited control over how their information is used or shared, particularly in cross-border collaborations where varying privacy standards complicate compliance. Since deep learning-based disease detection relies heavily on these data, ensuring their privacy is of the utmost importance since the potential risks associated with the breach of such data can be detrimental, resulting in various negative outcomes, including the disruption of crop yields, financial crises, compromised farming business strategies, unfair competition, and even threats to food and financial security [18] intelligence (AI) in agriculture, particularly for disease detection in crops, has made significant strides, as evidenced by various studies (Table 1) employing methodologies like convolutional neural networks (CNNs) and spectral analysis. These advanced AI-based technologies offer promising accuracy in identifying diseases in crops such as wheat, corn, potatoes, and more, with some models achieving accuracy ratings as high as 91% to 98.9% (Table 1). ...
... Farmers frequently lack clarity about data ownership and may have limited control over how their information is used or shared, particularly in cross-border collaborations where varying privacy standards complicate compliance. Since deep learning-based disease detection relies heavily on these data, ensuring their privacy is of the utmost importance since the potential risks associated with the breach of such data can be detrimental, resulting in various negative outcomes, including the disruption of crop yields, financial crises, compromised farming business strategies, unfair competition, and even threats to food and financial security [18]. Therefore, while intelligence (AI) in agriculture, particularly for disease detection in crops, has made significant strides, as evidenced by various studies (Table 1) employing methodologies like convolutional neural networks (CNNs) and spectral analysis. ...
Article
Full-text available
Crop failure is defined as crop production that is significantly lower than anticipated, resulting from plants that are harmed, diseased, destroyed, or influenced by climatic circumstances. With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. However, deep learning seems to be a largely complex black box to interpret, necessitating a prerequisite for the groundwork of the model’s interpretability. Considering this, the aim of this study was to follow up on the establishment of a robust deep learning custom model named CropsDisNet, evaluated on a large-scale dataset named “New Bangladeshi Crop Disease Dataset (corn, potato and wheat)”, which contains a total of 8946 images. The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. In addition, demonstration of the model’s interpretability gave us insight into our model’s decision making and classification results, which can allow farmers to understand and take appropriate precautions in the event of early widespread harvest failure and food crises.
... Keamanan dan privasi data dalam jaringan IoT [11], [27], [29], [30], [31], [32] • Sistem enkripsi seperti AES-256 (Advanced Encryption Standard) adalah enkripsi yang banyak digunakan untuk menjaga kerahasiaan data. TLS/SSL (Transport Layer Security) adalah enkripsi untuk protokol komunikasi seperti MQTT dan HTTP. ...
... Namun, keberhasilan desain ini sangat tergantung pada implementasi keamanan dan privasi data yang kuat [11], [27], [29], [30], [31], [32]. Penggunaan enkripsi seperti AES-256 dan autentikasi berbasis sertifikat digital sangat penting untuk melindungi data dari ancaman siber. ...
Article
Full-text available
Abstrak-Rantai pasok pertanian adalah salah satu elemen penting dalam ketahanan pangan global, namun menghadapi tantangan besar seperti pemborosan pangan, perubahan iklim, dan permintaan akan transparansi. Internet of Things (IoT) muncul sebagai solusi inovatif untuk mengatasi tantangan tersebut dengan memungkinkan pelacakan produk secara real-time melalui sensor, gateway, dan platform berbasis cloud. Penelitian ini menggunakan pendekatan Systematic Literature Review (SLR) untuk menganalisis beberapa artikel yang relevan, dengan tujuan mengeksplorasi elemen-elemen utama, tantangan, dan strategi dalam desain jaringan IoT untuk rantai pasok pertanian. Hasil penelitian mengidentifikasi lima elemen utama dalam desain jaringan IoT: teknologi komunikasi, arsitektur jaringan, keamanan data, efisiensi energi, dan interoperabilitas. Teknologi seperti Low-Power Wide-Area Networks (LPWAN) menjadi solusi utama untuk wilayah pedesaan dengan keterbatasan infrastruktur. Tantangan utama yang dihadapi dalam implementasi jaringan IoT di sektor pertanian seperti keterbatasan infrastruktur komunikasi, keamanan dan privasi data, kompleksitas manajemen data, dan biaya implementasi yang tinggi. Strategi yang dapat digunakan untuk meningkatkan efisiensi dan keandalan jaringan IoT dalam rantai pasok pertanian seperti penerapan edge computing, integrasi blockchain, optimasi protokol jaringan, teknologi hemat energi, pendekatan hybrid networks. Implementasi IoT dalam rantai pasok pertanian memberikan dampak positif signifikan, termasuk pengurangan pemborosan, peningkatan transparansi, dan optimalisasi sumber daya. Studi ini menyimpulkan bahwa desain jaringan IoT yang optimal sangat penting untuk mendukung keberlanjutan dan efisiensi sistem rantai pasok pertanian, terutama di negara berkembang. Rekomendasi untuk penelitian selanjutnya mencakup integrasi IoT dengan kecerdasan buatan dan pengembangan teknologi hemat energi untuk memperluas adopsi IoT secara global. Kata Kunci-Internet of Things (IoT), Rantai Pasok Pertanian, Teknologi Komunikasi, Infrastruktur Komunikasi, Keamanan Data.
... Industry-wide standards and best practices for data security are crucial for building trust and safeguarding sensitive data. Regular audits and assessments can help maintain the integrity of these systems and instill confidence in farmers regarding the security of their data [148][149][150] ...
... As precision agriculture relies heavily on data-driven insights, ensuring the security and traceability of this data is paramount [149]. Blockchain technology emerges as a potential solution to enhance data security in agriculture. ...
Article
Full-text available
Precision agriculture, a transformative farming approach, has gained prominence due to advancements in digital technologies. This paper explores the multifaceted landscape of precision agriculture, focusing on its tangible benefits, challenges, and future directions. Purpose Amidst the growing interest in precision agriculture, this paper aims to provide a comprehensive analysis of its various aspects. Specifically, it seeks to elucidate the benefits of precision agriculture in optimizing resource utilization, enhancing crop health, and promoting sustainability. Moreover, it examines the challenges faced in its implementation and proposes future directions to overcome these obstacles. Findings Through a review of existing literature and case studies, this paper presents a nuanced understanding of precision agriculture's impact on crop farming, livestock production, economic outcomes, and environmental sustainability. It identifies key challenges such as data security, implementation costs, and regulatory frameworks, while also highlighting innovative solutions and promising advancements in the field. Originality To the best of our knowledge, this paper represents a rigorous attempt to comprehensively analyze the landscape of precision agriculture, with a focus on its original contributions to the field. By synthesizing existing research and offering insights into future directions, it adds to the emerging knowledge base surrounding precision agriculture and its potential to revolutionize modern farming practices.
... But it's not just meat producers at risk. The same year, the BlackMatter ransomware group (Hartley, 2022;Hazrati et al., 2022). This attack threatened to cripple grain storage and animal feed operations, demonstrating how cybercriminals can exploit agricultural cycles for maximum impact. ...
Article
Full-text available
The rapid digital transformation of dairy and poultry farming through big data analytics and Internet of Things (IoT) innovations has significantly advanced precision management of feeding, animal health, and environmental conditions. However, this digitization has simultaneously escalated cybersecurity vulnerabilities, presenting serious threats to economic stability, animal welfare, and food safety. This paper provides an in-depth analysis of the evolving cyber threat landscape confronting digital livestock farming, examining ransomware incidents, hacktivist interference, and state-sponsored cyber intrusions. It critically assesses how compromised digital systems disrupt critical farm operations, including milking routines, feed formulations, and climate control, profoundly impacting animal health, productivity, and consumer trust. Responding to these challenges, we present a comprehensive cybersecurity roadmap that integrates established IT security practices with agriculture-specific requirements. The roadmap emphasizes advanced solutions, such as AI-driven anomaly detection, blockchain-based traceability, and integrated cybersecurity-biosecurity frameworks, tailored explicitly to safeguard livestock farming. Additionally, we highlight human-centric elements such as targeted workforce education, rural cybersecurity capacity building, and robust cross-sector collaboration as indispensable components of a resilient cybersecurity ecosystem. By synthesizing technical advancements, regulatory perspectives, and socio-economic insights, the paper proposes a proactive strategy to enhance data integrity, secure animal welfare, and reinforce food supply chains. Ultimately, we underscore that effective cybersecurity is not merely a technical consideration but foundational to ensuring the sustainable, ethical, and trustworthy advancement of livestock agriculture in a data-driven world.
... Ensuring the security of farm data is crucial. This includes implementing robust data protection measures such as encryption, secure passwords, and restricted access to sensitive information (Hazrati et al., 2022). Protecting data from unauthorized access and cyber threats is essential to maintain the integrity of farm records. ...
Chapter
Effective dairy farming necessitates a balance of various tasks, from feeding and milking to animal handling and machinery maintenance. Every activity impacts milk production, making precise management crucial. This chapter explores the significance of meticulous record-keeping in dairy farming, highlighting its role in enhancing decision-making, compensating for memory limitations, and facilitating animal identification. Key record categories include milk output, reproduction, feed costs, health records, financial reports, etc. They all contribute to farm productivity and efficiency. There are a myriad of advantages of computerised record-keeping. These include efficiency, optimization, data accuracy, customized reporting, etc. Animal and farm data is utilized to enhance productivity, manage health and welfare, improve financial and operational efficiency, support strategic decision-making, ensure compliance and certification, integrate technology, and promote sustainability. Some issues that include cover training, data security, and legal compliance must, however, be considered. It is concluded that record-keeping in the dairy sector plays a pivotal role in detailed and accurate records in modern dairy farming, ensuring the health, productivity, and profitability of dairy operations.
... PLF technology also faces numerous issues, such as data protection and security. It is imperative to safeguard sensitive information, such as animal health records and farm management procedures, from illegal access, hacks, and breaches (Fielke et al., 2020;Hazrati et al., 2022). Farmers must purchase equipment such as sensors, monitoring devices, global positioning system (GPS) trackers, and milking robots, so the initial cost of PLF technologies is a major challenge for small-scale farmers (Papakonstantinou et al., 2024). ...
Article
Full-text available
This study aimed to review the recent development of different technologies in precision livestock farming (PLF), along with their scopes and challenges. PLF is an innovative, contemporary, and fast-expanding approach to agriculture that aims to improve sustainable livestock farming. The growing global population has increased the demand for animal products. To meet this demand, farmers have to increase their production, so without the integration of precision technology, this cannot be achieved. PLF currently employs a variety of technologies. Some of these methods include vision-based solutions, load cells, accelerometers, microphones, thermal cameras, photoelectric sensors, and radio-frequency identification (RFID). Despite the availability of different PLF technologies, their adoption by farmers varies widely on the basis of the cost of investment, ease of operation, availability, and accessibility. These technologies are used to track different activities of livestock farming, such as feeding, drinking, physical behavior, temperature regulation, tracking and identification, estrus detection, disease detection, and milking. PLF contributes significantly to technological advancement, human-animal relationships, environmental sustainability, and increased productivity. It does, however, present a number of obstacles and eventual advantages.
Chapter
Agriculture is a fundamental global primary industry, evolving with advances in ICT and automation. The rise in ‘smart farming' integrates cutting-edge technologies like IoT, drones, sensors, GPS, big data analytics, and AI to improve efficiency, productivity, and sustainability. These innovations facilitate real-time monitoring, data-driven decisions, and automation in key farming tasks, including soil analysis, irrigation, crop health evaluation, and pest management. However, the adoption of smart technologies introduces cybersecurity risks. This chapter explores cybersecurity threats in smart farming in regional Australia. As farmers increasingly rely on advanced technologies such as IoT, drones and satellites, blockchain, and robotics, implementing cybersecurity measures is vital. Without robust security, farmers may lose trust in these technologies. Farm data and IT assets require raising awareness, promoting best practices and integrating cybersecurity into agricultural systems.
Article
Ensuring the security of sensitive or private information is crucial to prevent malicious tampering, especially in multimedia applications like intelligent transport systems (ITS), which are vital components of a smart city. These systems can be vulnerable to traffic management and rerouting techniques that manipulate the images captured by roadside units. To address this challenge, this paper introduces advanced image encryption algorithms designed specifically for securing image manipulation and transmission in roadside ITS units. Initially, a sequential version of the algorithm is proposed, demonstrating a high level of confusion achieved through the chosen coding method (chromosomal representation). This sequential approach results in maximum interference between the original image and its encrypted counterpart, with an entropy level of 7.95, nearing the optimal value of 8. To improve computational efficiency, three additional algorithms are presented, utilizing parallelization based on the islanding model, both with and without migrations. The algorithms are designed to enhance security by increasing confusion and incorporating genetic diffusion. The performance and security of these algorithms are evaluated using established methods such as information entropy, differential attack analysis, and key space analysis. Our algorithms have also shown a strong ability to maintain performance and robustness even in the presence of noise. Furthermore, they exhibit superior resistance to attacks compared to recent competitive approaches. In summary, the proposed algorithms offer robust protection against image manipulation and unauthorized access in roadside ITS units, thereby contributing to the overall security and reliability of smart city infrastructure.
Conference Paper
Full-text available
In the technology today, user-based authentication and password are now widely used in all information systems and services. Most of the university also uses this type of authentication method for many services, but the password is in danger. By providing a password-protected verification system for the most usable and secure organization. In the old days, the password was used as the best authentication system to prevent unauthorized access. Now the technology in the authentication system is growing day by day so that the password is changed to be more secure. However, the vulnerability of this traditional system has prompted the industry and researchers to find a new alternative where there is no threat such as theft, hacking and cracking passwords. This study discusses in more detail the key strategies for verifying the authenticity of a password in detail and sets out an attempt to explain details and process of each technology. The paper consists of an extensive review of the research conducted in past several years and this research study has presented a review of recent research works which are mainly conducted for improvising the security with the end to end encryption process. Keywords— Traditional Authentication, Password less Authentication, Emerging Authentication, Biometrics, Web Security
Article
Full-text available
The exponential rise of information available means we can now, in theory, access knowledge on almost any question we ask. However, as the amount of unverified information increases, so too does the challenge in deciding which information to trust. Farmers, when learning about agricultural innovations, have historically relied on in-person advice from traditional ‘experts’, such as agricultural advisers, to inform farm management. As more farmers go online for information, it is not clear whether they are now using digital information to corroborate in-person advice from traditional ‘experts’, or if they are foregoing ‘expert’ advice in preference for peer-generated information. To fill this knowledge gap, we sought to understand how farmers in two contrasting European countries (Hungary and the UK) learnt about sustainable soil innovations and who influenced them to innovate. Through interviews with 82 respondents, we found farmers in both countries regularly used online sources to access soil information; some were prompted to change their soil management by farmer social media ‘influencers’. However, online information and interactions were not usually the main factor influencing farmers to change their practices. Farmers placed most trust in other farmers to learn about new soil practices and were less trusting of traditional ‘experts’, particularly agricultural researchers from academic and government institutions, who they believed were not empathetic towards farmers’ needs. We suggest that some farmers may indeed have had enough of traditional ‘experts’, instead relying more on their own peer networks to learn and innovate. We discuss ways to improve trustworthy knowledge exchange between agricultural stakeholders to increase uptake of sustainable soil management practices, while acknowledging the value of peer influence and online interactions for innovation and trust building.
Article
Full-text available
Internet of Things (IoT) presents opportunities for designing new technologies for organizations. Many organizations are beginning to accept these technologies for their daily work, where employees can be connected, both on the organization's premises and the “outside”, for business continuity. However, organizations continue to experience data breach incidents. Even though there is a plethora of researches in Information Security, there “seems” to be little or lack of interest from the research community, when it comes to human factors and its relationship to data breach incidents. The focus is usually on the technological component of Information Technology systems. Regardless of any technological solutions introduced, human factors continue to be an area that lacks the required attention. Making the assumption that people will follow expected secure behavioral patterns and therefore system security expectations will be satisfied, may not necessarily be true. Security is not something that can simply be purchased; human factors will always prove to be an important space to explore. Hence, human factors are without a doubt a critical point in Information Security. In this study, we propose an Organizational Information Security Framework For Human Factors applicable to the Internet of Things, which includes countermeasures that can help prevent or reduce data breach incidents as a result of human factors. Using linear regression on data breach incidents reported in the United States of America from 2009 to 2017, the study validates human factors as a weak-point in information security that can be extended to Internet of Things by predicting the relationship between human factors and data breach incidents, and the strength of these relationships. Our results show that five breach incidents out of the seven typified human factors to statistically and significantly predict data breach incidents. Furthermore, the results also show a positive correlation between human factors and these data breach incidents.
Article
Full-text available
Smart agriculture integrates a set of technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, cloud, and edge computing provide capabilities and solutions to keep, store, and analyze the massive data generated by components. However, smart agriculture is still emerging and has a low level of security features. Future solutions will demand data availability and accuracy as key points to help farmers, and security is crucial to building robust and efficient systems. Since smart agriculture comprises a wide variety and quantity of resources, security addresses issues such as compatibility, constrained resources, and massive data. Conventional protection schemes used in the traditional Internet or Internet of Things may not be useful for agricultural systems, creating extra demands and opportunities. This paper aims at reviewing the state-of-the art of smart agriculture security, particularly in open-field agriculture, discussing its architecture, describing security issues, presenting the major challenges and future directions.
Article
Full-text available
Trust is a vulnerability and, like all vulnerabilities, should be eliminated. Zero trust is a systemic approach to information security that trusts no user, transaction, or network traffic unless verified.
Article
Full-text available
With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture ( precision agriculture, facility agriculture, and order agriculture ) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures ( authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems ) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies ( 5 G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture ) are described as the future research directions of smart agriculture.
Article
Full-text available
Agricultural cybersecurity is a rising concern because farming is becoming ever more reliant on computers and Internet access. During the last few years, the agrotechnology community, public sector, and researchers have been alerted to the problem and a significant amount of research has focused on the issue. However, the majority of the existing work focuses on external threats or specific parts of the farm technology ecosystem. This work examines the cybersecurity capabilities of individual farms and focuses on the farm local area network; the network and connected devices of six dairy farms in Finland are examined in detail. In addition, the farmers were interviewed in order to ascertain their opinions and understanding of agricultural cybersecurity. The results of the reviews were mixed. The physical cabling, for example, was all in good condition and followed appropriate regulations. On the other hand, network topology, malware protection, and system backups were not handled appropriately. Surveillance cameras typically did not work as expected. Often, the farmers did not know the network topology, the connected devices, or the details of individual devices in the network. In summary, the cybersecurity on the farms reviewed in this work was not handled optimally and significant improvements would be needed in order to secure the reviewed systems. However, since the approach of this work is qualitative in nature, care must be taken when generalizing the results. In conclusion, there is a significant need for improvements in agricultural cybersecurity on the level of individual farms. Many of the threats faced by farms are caused by their own activity or the physical environment and thus, emphasis must be put on improving their own situations.
Article
Food security is a societal issue of increasing importance requiring careful consideration of the way we produce, process, and distribute food [2], [6]. Digital technologies are increasingly used to optimize processes to support these activities, and therefore, bear important implications for food security. In this article, we focus in particular on technologies used for an important aspect of such security - the effective production of sufficient food, or food availability. Technology has always been an integral part of humanity's efforts to optimize food production processes. Already thousands of years ago, farming tools such as plows were used, first by humans, then with animals, to improve conditions for planting crops and thereby increase yields. Modern food production has seen an explosion of both the amount and the sophistication of technologies used, and has increasingly moved to sophisticated digital technologies such as robots, sensor-driven systems, drones, and automated image analysis [11].
Article
Agriculture production highly depends on water and soil factors which increasingly need to be utilized efficiently. Precision agriculture, through the set of information technologies that it uses, allows to effectively manage these resources. This work aims to gather the existing knowledge on technologies used in precision agriculture and ways to discern the most appropriate one for different contexts in agricultural processes. A systematic literature review is performed to identify precision agriculture implementations and to answer questions such as the type of technologies used, criteria for their comparison and selection, and the existence of frameworks that help to decide what technologies to implement. A total of 3,949 publications were reviewed, of which 259 addressed the posed research questions. The findings are that remote sensors are the most used technology, the required knowledge is an important criterion for deciding to implement precision agriculture, and no framework was found that guides its implementation.
Article
The Internet of things (IoT) aims to connect everything and everyone around the world to provide diverse applications that improve quality of life. In this technology, the preservation of data privacy plays a crucial role. Recently, many studies have leveraged machine learning (ML) as a strategy to address the privacy issues of IoT including scalability, interoperability, and resource limitation such as computation and energy. In this paper, we aim to review these studies and examine opportunities and concerns related to utilizing data in ML-based solutions for privacy in IoT. We, first, explore and introduce different data sources in IoT and categorize them. Then, we review existing ML-based solutions that are designed and developed to protect privacy in IoT. Finally, we examine the extent to which some data categories have been used with ML-based solutions to preserve privacy and propose other novel opportunities for ML-based solutions to leverage these data sources in the IoT ecosystem.