IoT Powered Agricultural Cyber-Physical System: Security Issue Assessment

Abstract

Smart farming or development in agriculture Cyber-Physical Systems has led to the development of various IOT based innovative platforms to aid automated systems for precision agriculture. There are smart devices (IoT devices, smart vehicles, UAVs, ROVs, and drones) and sensors (magnetic, electrochemical, and mechanical sensors)constantly connected at the edge and procure data that is uploaded to cloud-based interfaces for further processing. The sensors can be maneuvered using an insecure, intelligent farming system to create choreographed cybersecurity attacks on a particular farm. End-to-end system is achieved by putting together smart devices, communication media, uploading data to the cloud, intermediate nodes, and processing this data at a central unit like the cloud or intermediate processing at other nodes. Cyber-physical systems are a combination of many technologies, resulting in a large number of security threats. This chapter is to delineate the attack vectors and categories of threats that can be performed on these innovative IOT based agricultural products; if not appropriately secured, can lead to more strategized and sophisticated security attacks that can hinder production for a region under effect and thus bringing down the economy and food stocks of Nations who implemented smart agriculture.

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IoT Powered Agricultural Cyber-Physical System:
Security Issue Assessment
Elham Kariri
To cite this article: Elham Kariri (2022): IoT Powered Agricultural Cyber-Physical System: Security
Issue Assessment, IETE Journal of Research, DOI: 10.1080/03772063.2022.2032848
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IETE JOURNAL OF RESEARCH
https://doi.org/10.1080/03772063.2022.2032848
IoT Powered Agricultural Cyber-Physical System: Security Issue Assessment
Elham Kariri
Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi
Arabia
ABSTRACT
Smart farming or development in agriculture Cyber-Physical Systems has led to the development
of various IOT based innovative platforms to aid automated systems for precision agriculture. There
are smart devices (IoT devices, smart vehicles, UAVs, ROVs, and drones) and sensors (magnetic, elec-
trochemical, and mechanical sensors)constantly connected at the edge and procure data that is
uploaded to cloud-based interfaces for further processing. The sensors can be maneuvered using
an insecure, intelligent farming system to create choreographed cybersecurity attacks on a particu-
lar farm. End-to-end system is achieved by putting together smart devices, communication media,
uploading data to the cloud, intermediate nodes, and processing this data at a central unit like the
cloud or intermediate processing at other nodes. Cyber-physical systems are a combination of many
technologies, resulting in a large number of security threats. This chapter is to delineate the attack
vectors and categories of threats that can be performed on these innovative IOT based agricul-
tural products; if not appropriately secured, can lead to more strategized and sophisticated security
attacks that can hinder production for a region under effect and thus bringing down the economy
and food stocks of Nations who implemented smart agriculture.
KEYWORDS
IoT security; IoT security
threats; Smart agriculture;
Smart farming; Threat
intelligence
1. INTRODUCTION
Agriculture practices have seen exponential growth as
smart technologies have been implemented in the past
decades. The evidence of technology being involved in
various steps and tiers of food production has shown
reliability on these innovative systems for adding addi-
tional ease in production. Various modules and tiers are
conjugated together to implement the functionality. This
chapter highlights the attack vectors and threat mecha-
nisms that can be devised to spike or introduce subtle
or negligible changes to disrupt the systems and ulti-
mately aect the infrastructure at par. The ideology is to
segregate and review various threat vectors that can be
exploited on IOT based smart agriculture systems, such
as exploitations of any vulnerabilities or aws to com-
promise the system integration. In a basic IoT system,
multiple parties gather, process, and store information;
thesefunctionalitiesareeitherhandledbyasinglehandler
or can be moduled and bought from third-party ven-
dors [1]. A resultant can be easily explored for loopholes
to disrupt the services or cause damage to the system and
its dependencies Figure 1.
The majority of the IoT systems in farming are generally
related to monitoring values such as soil humidity, soil
Ph, temperature, luminosity. The sequence of events is
generally monitored at the ground level and data pass-
ing through various nodes for nal analysis to perpetuate
results. These results are then used to make decisions that
allow the handlers/users to take necessary actions such as
whether to irrigate, fertilize the elds or not, and whether
to regulate temperature or not [2]. This data is also stored
for further check of future possibilities in crop yield, giv-
inganoverviewofwhatconditionsonaground-level
suited better to gain more yield in an area compared to
lab-controlled environments. Undoubtedly, these devices
have provided much-needed benets and real-time mon-
itoring ease for current agricultural practices, shaping for
more gains in the future Figure 2.
Figure 3enlists the various layers present in any IoT-
based system used in agricultural systems. The physical
layerismainlymadeupofdevicessuchassensorsrecord-
ing values at their end. Generally, these are raw values
and often need processing for further usage. The physical
layer ensures these sensors are connected on the ground
and working in a calibrated manner for optimal system
performance [4]. The values such as soil humidity, tem-
peratures, ph values, etc., general observations from sen-
sorsarerecordedatspecictimeintervals.Thephysical
layer is further connected to the network layer, as the raw
data cannot be processed by sensor nodes and has to be
© 2022 IETE

2 E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT
Figure 1: Architechure model of CPS
propagated to the next tier, which is the middle layer; this
is often done via technologies of wireless band depend-
ing upon the location and feasibility to run the network
layer without much data loss. Often rural farms might
nothavepropernetworkconnectivityandrelyonslower
networks [1,3]. The middle layer is mainly introduced
to ease the processing burden so that any pre-processing
can be done beforehand. The Application layer is majorly
for nal presentation to user or administrator, decision
making, result analysis [1,5]. This chapter will explore the
attack vectors/vulnerabilities or threat landscape of smart
agriculture systems. Section 1, as the introduction, relates
Figure 3: Estimated IoT adoption in Agricultural sector in terms
of percentage globally
to the architecture used on IoT smart systems;the idea
istoelaborateonhowthetier-wisearchitecturecanbe
explored on various levels of threats. Section 2enunciates
past work done towards ensuring security, mainly during
the use of IoT; researchers have worked on wireless sensor
networks as they are the backbone of IoT-built systems
and provide for the communication channel between lay-
ers. Section 3presents a detailed threat landscape that
highlights the variety of vulnerabilities and attacks with
case studies. Finally, section 4refers to mitigation and
practices involved.
2. LITERATURE REVIEW
ThechangingreliabilityofIoTsmartagriculturesys-
temshasbroughtinthemajorityofchallengesonthe
forefront as the economy for the country would often
depend on the agricultural produce and yield. Since a
major part relies on technology, it becomes a more lucra-
tive opportunity to attack these interfaces. Therefore,
Figure 2: Layers in IoT based smart agriculture system [3]

E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT 3
the IoT technology which has been deployed for aid-
ing might be jeopardized. These systems are often made
to sustain the ground scenarios and be cost-ecient;
this leaves concern for the security of such devices [6].
Mainly, the usage of wireless sensor networks(WSNs) and
securing their communication has been worked upon in
recent years; apart from WSN; data protection has been
another worked upon feature. Since the WSNs limit their
processing power, it is important to secure the chan-
nels to ensure proper management, [7]havepresented
a schema against impersonation and insider attack by
applying two-factor authentication methods. However,
it lacked any high-level encryption schemes which can
easily be cracked using cryptanalysis. Another authenti-
cation scheme was presented by [8]tocombattheauthen-
tication and encryption problem by using remote authen-
tication processes. Eventhough these schemes were used,
but the threat of impersonation could still lead to spoof-
ing and man-in-the-middle attacks. Ref. [9]hasproposed
a session key management to secure the authenticated
channel and secure it against password attacks. Ref. [10]
proposed a novel remote authentication scheme using
BAN logic and session key management, automated vali-
dation of internet security protocols and applications is
used to provide this functionality. In [11], the authors
have worked upon secure user authentication alongside
WSN for secure monitoring of agricultural systems. The
system rstly veries the registered user with a smart-
card mechanism; this smartcard is authenticated via a
card reader to ensure the legit person is accessing the sys-
tem alongside giving a password set by the user. Ref. [12]
have researched possible usage of blockchain to aid the
possibility to draw smart contracts and store data for pro-
cessing and post-processing to smart ledgers to ensure
enhanced security. As more and more data is being pro-
cessed at edge and cloud nodes, it has become easier
tousethisfortheledgerandapplicationlayerbetween
twoparties.Ref.[13]haveproposedasystemtouse
blockchain as the main data collection point from var-
ious sensor nodes, smart contracts are drawn between
management nodes to capture data from sensors and
analyze it. Another ecient approach of creating clus-
ters has been explored by [14,15]throughLowenergy
adaptive clustering hierarchy(LEACH), in the second
phaseofexecution,thedataisseparatedintomulti-
ple blocks uploaded as ledgers on the blockchain, hence
securing the data them. Subsequent nodes will prop-
agate data using Voronoi architecture; the methodol-
ogy has also reduced the update of cluster data only
when a new node is added and not advertising time
andagain.Ref.[16] have implemented a secure remote
telemetry-based subsystem that can easily upload data via
a secured channel through SSH/TLS, the storage system
Figure 4: Elements of Cyber-Physical System
has been secured through a rewall, and IKE authentica-
tion through VPN has been used to propagate data from
node to node. Furthermore, isolation has been estab-
lished in the system using containerization-based SAAS
andPAASoeringsoncloudinterfaceFigure4.
3. THREAT LANDSCAPE
Considering the threats that pertain to IoT systems, the
threats can be categorized through four layers as specied
in Figure 5above, the vulnerabilities in IoT-based sys-
tems are more as they are mainly remotely located and
comprise various components [17–19]. Therefore, it is
important to protect against such threats; Figure 5shows
a brief scenario of what attacks can be more prominent at
which layers.
3.1 Physical Layer/ Perception Layer Attacks
The physical layer is generally exposed to attacks that can
be done directly on the sensors, whereas there could be
eminent manipulations on parts of sensors and actuators;
this can easily change the crop’s analysis and lead to dire
consequences [21–23]. The sensors are mainly designed
to fabricate a single task and are not used to processing
any overhead tasks; it is challenging to keep sensors in
safe places.

4 E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT
Figure 5: Threat Landscape for IOT-based smart agriculture devices [2,16,20]
3.1.1 Node Tampering/Jamming
Equipment such as actuators, sensors are generally
embedded in nature, having a single task to perform.
Therefore, these nodes cannot have more security fore-
front.Attackslikenodetampering,jammingaremore
prevalent. In such an attack, the data generated at the
hardware is modied, which can set o an absolute dif-
ference in the data, further sent to higher layers for pro-
cessing. Refs. [24–26] has discussed the usage of strong
malicious nodes within the network to launch a tam-
per attack against existing nodes in the network. Such an
attack can be detected by observing a signicant drop in
packets at the sink node [27–29].
3.1.2 Interferences/Insider Attacks
The perception layer in IoT is more susceptible to
insider attacks wherein eavesdropping; interferences can
be introduced by injecting an external agent in disguise.
Ref. [30] have published the severity of insider attacks,
wherein it was observed that these attacks could often
lead to drastic consequences if left unchecked in critical
IoT systems.
3.1.3 Node Injection/Code-Based Manipulation
Injection-based attacks are often performed to spike
or manipulate the values at lower layers that can
go undetected and cause larger magnied dierences,
Ref. [31]denesthisasakindofsalamiattackthatcan
easily magnify in the long run. For instance, spiking the
values of fertilizers by a very insignicant amount, but in
the long run, the value will show a larger increase and can
compromisetheyieldqualityorleadtocropdestruction.
3.1.4 Physical Damage
Physical damage to equipment can be natural as a result of
calamity or otherwise. It can lead to null values at receiv-
ing end or missed values, which can cause an imbalance
in the real-time processing of data. Ref. [32]described
how a drone can be controlled to take over and remotely
cause failure to other nodes in its vicinity through an
insecure UDP channel.
3.2 Network Layer Attacks
This layer is more prevalent in common wireless sen-
sorattacksastheunderlyinghardwareislowprocessing.
It makes it easier to perform eavesdropping, sning,
spoongpotentially;causingdamagetodatabeingsent
or disruption of services to underlying nodes leading to
missing data [33].
3.2.1 Traffic Analysis/Wireless Sensor Node Sniffing
MostIoT devices work on low power and have low pro-
cessing capabilities, generally limited to single function-
ality; this remains a challenge to secure the intermediate
layer. [demo] has introduced a 6LoWPAN based intru-
sion detection system that can detect and alert over IoT

E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT 5
node-based 6LoWPAN; the tests show higher accuracy in
detectingcommonsning-basedattacks.Ref.[34]shows
a simulation of sniers onto node deployments that use
graphtheorytondconvenientnodestosniinclose
locations.
3.2.2 Spoofing/Cloning
Spoongisanactivecategoryofthreatthatcancause
disruptions in the system, inject codes through inter-
faces to manipulate upper layers. Spoong in wireless
sensor network nodes can sometimes be achieved by cre-
ating a clone of legit nodes and entering the network as
an imposter. Ref. [35] shows how machine learning can
enhance learning-based detection, authentication, access
control system that can easily formulate alerts. They have
used SVM, Q-learning, K-NN, Naive Bayes to detect
and classify between various degrees of threats. Spoong
can cause potential data leaks,leading to data compro-
mise for users attached to the system. Ref. [36]have
explored spoong against actuators, leading to integrity
violation and DoS attacks after generating control-based
spoof attacks in the network. The method of protecting
actuators has been proposed using TEE[Trusted execu-
tion environment] that monitors all commands coming
from actuators via simplex communications and gen-
erates alerts or ignores according to conditions for the
trigger [37].
3.2.3 Unauthorized Access
To provide for crops through smart IOT based agri-
culture deployments, it is highly important to properly
monitor conditions in real-time. It needs constant checks
either by an automated system or user at their end; any
unauthorized access to this system might lead to a dras-
tic outcome. Chances of replay attacks [38], stolen cre-
dentials. might lead to any unauthorized person gaining
virtue on the system. Ref. [24]havesuggestedusingan
unspent transaction outputs model for carrying out a sin-
gle set of instructions, whereas an account-based online
transaction model can be used for parallel execution in
transactions.
3.2.4 Man-In-The-Middle
MITM is commonly performed through insecure wire-
less channels where the communication path is not
secure enough and can lead to packet capture, and any
unauthorized user can gain access to all the information
transferring over the insecure communication channel.
Once the attacker gains access, they can easily escalate
their privileges and gain access within the system to make
any irregular changes without the actual users noticing.
Ref. [39] has conducted security assessments on multi-
ple farm-based types of equipment with smart sensors;
they concluded that physical devices do come with added
security fabrication, but through the upper layers, these
implementations are in the hands of users, which creates
a niche for a mistake. Department of Homeland security,
US has also referred to threats of MITM attack due to
negligenceonthepartofuserswhileactivatingsecurity
obligations on their interfaces.
3.2.5 Denial of Service
According to [5], in precision agricultural systems, the
denial of service is more frequent towards users using
smart services; the threats can be due to poorly man-
aged systems, disruption to PNT(positioning, navigation,
and timing) systems that use GPS to collect information
for processing. Other disruptions can be accounted for
at supply chains or communication channels in decision
support systems.
3.3 Middle Layer/Edge Layer
This layer generally consists of edge level processing, a
practice of pre-processing data before uploading to cloud
services; it is important to take careful security mea-
suresatthislayer[11]. The methodology of securing the
parameter consists of safekeeping data with encryption
policies, transmitting data through safe means, maintain-
ing data integrity at each node. Ref. [40]haveproposed
creatinga trust execution environment for edge devices
by creating a trust zone at the real-time operating system’s
thread level. Ref. [41] have used virtualization techniques
at edge level to lightweight implementation, decreas-
ingoverheadduringtransmissionofinformation,using
Docker containerization, and building single-purpose
appliances for ease of use.
3.3.1 Encryption
Encryption of contents is extremely important when the
data is transmitted or stored; the practice helps to secure
the parameter to a vast extent. Ref. [11]haveshown
hybrid usage of hardware-based AES algorithm before
transmission of data alongside the second layer of protec-
tion by adding public key protected with RSA algorithm.
It is emphasized that an edge layer is maintained near the
IoT devices to decrease the computation and communi-
cation load. In [6], an edge-aided searchable public-key
encryption is created that uses bilinear mapping initially
and in the second phase at the edge side uses a strat-
egy of oine-online schema, the pre-computed data in
oine phase is sent to online phase for second encryp-
tion. Ref. [42] have another approach of initiating hierar-
chical identity-based encryption to contain privacy and
further use blockchain to create a tamper-proof ledger to
propagatedataacrossanetwork.

6 E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT
3.3.2 Data Based Attacks
This category of threat is related to storage, data process-
ing, and new age smart precision agricultural machin-
eryusesmoderntechniquessuchasmachinelearning
and articial intelligence to enhance the eciency of
the systems and decrease the dependency on users for
manual integration. The process of automation requires
better data that can be used for prediction, which can
yield better results; the decision support systems often
require long-term data to determine decisions accord-
ingly. Ref. [43]havecreatedasmartsecuritysystemfor
agriculture-based IoT systems which uses an ethereum
server to store data instead of traditional servers; EVMs
are used to contain data coming from sensors and col-
lected in a single place; any queries from the user are also
answered through the same server. The key is to hash the
data coming from sensors initially to avoid any mid-way
manipulations.
3.3.3 Policy-Based Threats
In IoT-based implementations, there are various agents
combined to capitulate better results. These elements
need micro-managing through policies or alerts,
required to be congured at the user’s end. The major-
ity of the issues in security arise from weak passwords,
default passwords, non-encrypted methods of sharing
information, improper authentication, and authorization
policies.Ref.[44] have shown using real-time policy
management how nodes can be managed with dier-
ent alert-based strategies to control nodes in a network;
the implementation isbased on smart city and regula-
tion control using thresholds for speed, area coverage,
surveillance.
3.4 Application Layer
This layer is responsible for creating an interactive inter-
face between user and system. Thus, it is relatively crucial
to mark a stricter front onthe application side. Depend-
ing upon the operating systems and underlying hardware,
multiple vulnerabilities and exploits can be harnessed.
3.4.1 Social Engineering
Anaiveuserisoftenthemostinsecurepointinthe
complete network; as [5] states how commercial farming
practices require more sophisticated equipment for better
agricultural practices, it is equally critical to ensure safety
from the user’s end. Phishing is another social engineer-
ing attack strategy that is generally used to lure the users
into clicking on synonymous links to gain access;In a
more targeted form, spear phishing is usually associated
with almost legit emails.
3.4.2 Code-Based Exploits
According to [45], a mere malicious code could easily
inject false data and project a dierent scenario. To deal
with this practice, they have used post-quantum cryptog-
raphy with parity check. In practice, an authentication
on the server is run to verify the user; afterwards device
anonymity and location privacy is practiced to ensure the
attacker cannot reach the legit nodes.
3.4.3 Cloud-Based Threats
As the data coming from sensors is used for real-time
analysis, it is much ecient if the cloud systems are
used for this purpose as they are ubiquitous, reduce
latency, and increase ease of access and availability.
The SAAS and PAAS platforms are devised to use ser-
vices and applications for real-time analytics, manage-
ment, decision-making. Mobile-based applications tend
to use permissions and target data from the user’s end;
ne-grained control must be established at the user’s
end to maintain secure data exchange [11]. Identity
privacy, location information, node compromise, and
layer cloning/ forwarding can be easily attributed; third-
party applications used from multiple cloud vendors
can extract extra information without the user’s knowl-
edge. Ref. [46] have explored gateway leaks in smart
home technologies and showed that secret data leaks
cannot be prevented due to a lack of interoperability
standards.
4. MITIGATION PRACTICES
The growing need to provide for a large population and
ensure less food waste has led to increasing reliabil-
ity on smart farming practices that involve machinery
and sophisticated equipment [7,16,47]. It has increased
upstanding towards IoT-based devices; the practices are
automated to reduce manual intervention and burden.
However, this has brought about vulnerabilities that can
be explored and exploited to aect the associated users.
Agriculture forms a large part of the economy; any dis-
ruptions or unprecedented crop failure can impact the
economic growth of the country. If security breaches
towards such devices are ignored or left unchecked, it
could have repercussions on the economic prosperity and
lead to crop failure. According to OWASP’s index of top
ten vulnerabilities,the primary factors for insecure IoT
systems are weak passwords, insecure and unreliable net-
work usages, lack of security patch updates, outdated
components, and insucient privacy protection [48].
These collective factors have contributed to losses in IoT-
basedsystems.Table1enlists some of the suggested mit-
igations methods layerwise that have been implemented,

E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT 7
Tab le 1: Mitigation measures against security vulnerabilities
Sno Layer Method Protection against
1 Physical layer/ Perception layer Hardware hardening against eavesdropping by
cryptography using AES- counter and AES
Cipher block chaining
Ensures data encryption at the node, preserves
privacy, data integrity, traffic analysis
[16]
2 Physical layer/ Perception layer Security monitor for IoT controller, sensors, and
actuators
False authentication, weak passwords, security
patch updates, firmware extraction,
code-based manipulation
[49]
3 Physical layer/ Perception layer Implement built-in security, maintenance,
updating of firmware
Reverse engineering, insecure backdoor
attacks
[5,8]
4 Physical layer/ Perception layer Data hiding in interferences, covert commu-
nication; easy to implement in low power
devices
Eavesdropping, man-in-the-middle attack,
stealth attacks
[50]
5 Network Layer Radiofrequency fingerprinting based authen-
tication and secure channel communication
through wireless key generation
False authentication, unauthorized access,
node cloning, code-based manipulation
[32]
6 Network Layer Intrusion detection systems- Machine learning-
based, deep learning-based, signature-based;
method of constant vigilance over the network
in real-time
Denial of services attacks, immunity against
virus signatures, 6LoWPAN-based network
attacks, wormhole attacks, a Routing
protocol for lossy network attacks,
Distributed denial of service, traffic analysis
[51]
7 Network Layer IoT security protocols- Low power, less overhead,
lightweight
Network-based attacks [23,25,26]
8 Middle layer/ Edge layer Creation of sensor layer to monitor edge data using
MQTT protocol
Data tampering, data analysis, eavesdropping,
protocol-level attacks, denial of service
[43]
9 Middle layer/ Edge layer Software-defined networking at edge layer to
prevent malicious traffic
Denial of service, traffic analysis, distributed
denial of service
[52]
10 Middle layer/ Edge layer Blockchain-based mobile computing Denial of service, traffic analysis, distributed
denial of service
[53]
11 Middle layer/ Edge layer Traffic shaping policies for IoT devices according to
usage
code-injections, manipulations [54]
12 Middle layer/ Edge layer Advanced encryption strategies, lightweight
implementations; Attribute-based encryption
Data confidentiality, integrity, [55]
13 Application Layer Service level agreements, interoperability
standards for any data collection
secret data collection for maintenance by
third-party applications
[5]
14 Application Layer Fine-grained control policies and protocols, proper
user management
unauthorized access, social engineering, data
theft, password leaks
[31]
Figure 6: Cyber-Phisical System Internal Constrants [9]
tested, observed, and deployed on other IoT-based smart
systemsandcanbeperpetuatedforagriculture-based
systems Figure 6.
5. RESULTS AND OBSERVATION
TheauthorhasattemptedtousethemostpopularIoT
protocol system in order to nd out the best-suited
system for agricultural utility application to a large
Figure 7: Signal rangs vs data rate
extent.Based on calculated examinations and compari-
son,webelievethatthatthebestcommunicationprotocol
is the one that can be widely deployed, accepted, and has
clearly dened use-cases.
In Figure 7,avirtualcomparisonhasbeendevelopedto
understand Signal ranges in comparison with data rate
to actually determine the exact protocol as per the area of
application Figure 8.

8 E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT
Figure 8: Comparision table of the IoT protol under different parameter

E. KARIRI: IOT POWERED AGRICULTURAL CYBER-PHYSICAL SYSTEM: SECURITY ISSUE ASSESSMENT 9
6. CONCLUSION
This chapter explored the variety of threats that can be
exploitedtocausedisruptionsintheservicesrelatedto
those systems, the majority of remotely controlled nodes
areeitheruncheckedforsecurity,ortheusersarenot
using the built-in security features. The modern termi-
nology of precision agriculture encloses multiple embed-
ded systems providing for a single type of task and man-
aged by controllers and services; this revolutionizing dig-
ital model needs to adapt to tighter security measures
as negligence can have consequences and destruction of
crops. Although the extent of the potential threat model
has not been explored in this regard, it needs to empha-
size security measures.
DISCLOSURE STATEMENT
No potential conict of interest was reported by the
author(s).
FUNDING
The author extend their appreciation to the Deputyship for
Research & Innovation, Ministry of Education in Saudi Ara-
bia for funding this research work through the project number
(IF-PSAU-2021/01/18095).
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AUTHORS
Elham Kariri is assistant professor and
researcher with 10 +years of experi-
ence teaching courses on undergraduate
and postgraduate levels in Prince Sattam
bin Abdulaziz University. Supervised 5
BA research graduation projects and 4
Mawhibaprojects.Publishedover3arti-
cles in peer-reviewed journals.
Corresponding author. Email: e.kariri@psau.edu.sa;
elham.kareri@gmail.com