Content uploaded by Iraq Ahmad Reshi
Author content
All content in this area was uploaded by Iraq Ahmad Reshi on Jun 02, 2024
Content may be subject to copyright.
Leveraging AI and Blockchain for Enhanced IoT
Cybersecurity
Iraq Ahmad Reshi and Sahil Sholla
Department of Computer Science and Engineering
Islamic University of Science and Technology, 192122 Awantipora, Kashmir, India
rshiraq333@gmail.com
sahilsholla@gmail.com
Abstract. This book chapter presents an overview of the cybersecurity
concerns of the Internet of Things (IoT). It investigates how artificial
intelligence (AI) and blockchain technologies address these challenges.
This chapter describes the growing number of accessories in IoT and the
increasing sophistication of cyberattacks targeting these devices. Each of
these factors presents its own set of unique security challenges. Further-
more, we are investigating the potential benefits of incorporating AI and
blockchain into IoT cybersecurity. These advantages include improved
threat detection and response, increased data privacy and integrity, and
increased attack resistance. Moreover, we present a review of particular
novel approaches in the field. This chapter presents brief case studies
of AI and blockchain-based Internet of Things cybersecurity solutions.
These case studies show the practical applications and benefits of these
technologies in safeguarding Internet of Things environments. The chap-
ter provides insights into the changing landscape of cybersecurity for the
Internet of Things (IoT) and AI and blockchain’s role in mitigating cyber
threats in this sector.
Keywords: Blockchain, Smart contracts, Internet of Things, Machine
Learning, Threat detection
1 Introduction
The ”Internet of Things” (IoT) is a comprehensive framework encompassing a
sophisticated network of physical entities, such as diverse devices, autos, and
household appliances. Integrating electronics, software, sensors, and networking
capabilities establishes this network. Illustrations of such entities encompass a
diverse array of contrivances, transportation means, and domestic apparatuses.
The Internet of Things (IoT) possesses significant potential to transform soci-
etal operations, including enhanced efficiency, streamlined automation, and un-
paralleled connectivity. However, the Internet of Things (IoT) ecosystem faces
much skepticism over its capacity for revolutionary change. Academic researchers
face the challenging endeavor of establishing coherent regulations, standardized
frameworks, and practical management approaches to guide the progression of
2 Iraq Ahmad Reshi and Sahil Sholla
the Internet of Things (IoT) in a manner that fosters innovation and overcomes
any barriers that may impede its inherently dynamic characteristics. The pro-
liferation of networked devices has led to a significant surge in their overall
quantity, presenting a diverse range of security concerns. It is crucial to recog-
nize that the aforementioned challenges profoundly affect not only the security
of our community but also the privileges we enjoy [12].
1.1 IoT cybersecurity and its challenges
The enormous quantity and diverse range of Internet of Things (IoT) devices
pose a significant challenge in safeguarding their security. The absence of well-
defined security mechanisms, such as encryption, authentication, and authoriza-
tion, in Internet of Things (IoT) devices renders them vulnerable to cyberat-
tacks. Moreover, many Internet of Things (IoT) devices primarily focus on cost-
efficiency, resulting in potential limitations regarding essential resources such as
memory and computational power. Consequently, these constraints may hinder
the implementation of advanced security measures. The presence of diverse Inter-
net of Things (IoT) devices and protocols poses a significant challenge in estab-
lishing a cohesive security architecture encompassing the entire IoT ecosystem.
Wi-Fi, Bluetooth, and Zigbee represent a subset of the communication proto-
cols utilized by Internet of Things (IoT) devices. Additionally, numerous devices
incorporate proprietary protocols that still need to be more explicit regarding
widespread recognition. Consequently, security specialists need help in develop-
ing universally effective security solutions. Moreover, Internet of Things (IoT)
devices present an enticing opportunity for malicious actors because of their
capability to collect and transmit sensitive personal data, such as individual
names, addresses, and credit card details. Hackers can facilitate the unautho-
rized infiltration of additional devices and data within a network by utilizing
Internet of Things (IoT) devices as potential entry points [16]. In general, the
issue of cybersecurity in the Internet of Things (IoT) presents a complex and
formidable challenge that necessitates a comprehensive and holistic approach. It
is imperative to prioritize the safeguarding of individual devices as well as the
establishment of comprehensive security protocols. Manufacturers must priori-
tize security and incorporate safety considerations into the design of their goods
from the outset.
1.2 Importance of AI and blockchain in IoT cybersecurity
Both artificial intelligence (AI) and blockchain possess the capacity to signifi-
cantly enhance the security of the Internet of Things (IoT). The critical nature
of real-time identification and mitigation of cybersecurity vulnerabilities arises
from the continuous data collecting and transmission facilitated by Internet of
Things (IoT) devices. Algorithms grounded in machine learning can acquire the
ability to identify discernible patterns of behavior that signify an imminent cyber
attack, thus enabling them to proactively or reactively implement precautionary
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 3
or remedial measures. An exemplary demonstration of the utilization of artifi-
cial intelligence (AI) lies in the automated activation of alarms or shutdowns
as a response to dubious activities about Internet of Things (IoT) devices. In
contrast, blockchain technology is a decentralized and immutable ledger that
has the potential to enhance the security of Internet of Things devices. Utiliz-
ing blockchain technology enables the decentralized storage of data, ensuring
its immutability, and facilitates the transparent transfer of data among Internet
of Things (IoT) devices. Utilizing blockchain technology enhances the security
of centralized systems or databases by rendering it more challenging for hack-
ers to tamper with or modify the stored data. As a result of the implemented
security measures, the likelihood of cyberattacks is reduced. Furthermore, imple-
menting blockchain technology can enhance the authentication and permission
mechanisms for Internet of Things (IoT) devices. Information and resources are
restricted to authorized devices for confidentiality and security. Any unautho-
rized device is restricted from accessing confidential information or resources.
Implementing blockchain-based identity management systems can effectively es-
tablish secure authentication and authorization protocols for IoT devices, pre-
venting unauthorized access. Furthermore, using artificial intelligence (AI) and
blockchain technology has proven to be highly efficient in enhancing the security
of the Internet of Things (IoT) infrastructure. The utilization of artificial intel-
ligence for real-time detection and response to cybersecurity threats, along with
the implementation of blockchain technology for safe and decentralized record-
keeping and identity management systems, can significantly boost the security
and reliability of IoT devices.
2 IoT Cybersecurity Challenges
The ”Internet of Things” (IoT) refers to a network of interconnected physical
objects with built-in sensors, software, and other technologies, enabling them to
collect and exchange data. The Internet of Things (IoT) encompasses numer-
ous benefits but also introduces a range of cybersecurity vulnerabilities. Figure
1 illustrates the main obstacles. The susceptibility of numerous IoT devices to
assaults is attributed to their constrained processing and storage capacities. The
susceptibility of these gadgets arises from inadequate security measures, includ-
ing the utilization of weak passwords, the absence of protected communication
channels, and the usage of outdated software. The absence of a standardized
framework for Internet of Things (IoT) devices results in software and hard-
ware configuration variations. The need for standardization poses challenges in
implementing uniform security measures across diverse devices. The collection
and transmission of data by Internet of Things (IoT) devices give rise to signifi-
cant apprehensions over privacy and security. The potential compromise of this
information could have severe consequences. The governance and monitoring of
IoT networks pose significant challenges due to their dispersed and decentralized
character. This poses a challenge in promptly identifying and addressing security
concerns. The complex interconnectivity of the various devices, networks, and
4 Iraq Ahmad Reshi and Sahil Sholla
platforms comprising an Internet of Things (IoT) ecosystem presents a signifi-
cant obstacle in ensuring their security. Malicious actors have the potential to
exploit any vulnerability within the ecosystem. A prevalent deficiency observed
in IoT devices is their incapability to receive firmware upgrades, rendering them
vulnerable to post-publication problems. This leaves the gadgets open to attacks
for as long as they stay in use [11]. Collaborative efforts among hardware and
software manufacturers, network and infrastructure service providers, and end
users are necessary to identify effective resolutions for these challenges. The de-
ployment of security measures, establishment of standards, and promotion of
best practices will be crucial in ensuring the security and privacy of IoT devices
and the data they collect [16].
Fig. 1. IoT cybersecurity Challenges
2.1 Types of cyber threats in IoT
An increasing number of individuals use IoT (Internet of Things) devices in their
residential, professional, and critical public infrastructure settings. However, the
current landscape provides fraudsters with an increased scope to exploit vulner-
abilities in Internet of Things (IoT) devices, enabling them to initiate a diverse
range of cyber attacks. The increased vulnerability linked to the extensive distri-
bution of these devices. The following enumeration presents a limited selection
of the possible manifestations of cyber attacks against Internet of Things (IoT)
devices. The rapid expansion of Internet of Things (IoT) devices necessitates
implementing strong cybersecurity measures.
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 5
–Botnet, Botnets refer to networks of compromised devices under the control
of an attacker or a collective entity. Distributed denial-of-service (DDoS)
attacks, initiated from one of these devices, can disrupt large networks of
computers that host critical websites.
–Ransomware,A malicious software program designed to encrypt a user’s files
after that, demanding a monetary ransom in exchange for the decryption
key required to regain access to the contents. The infiltration of Internet
of Things (IoT) devices by ransomware can cause significant disruptions to
commercial operations and result in the theft of personal data.
–MITM Man-in-the-middle attacks, or MITM attacks, manifest when an
unauthorized individual interposes within the communication channel, con-
necting two parties to manipulate or pilfer data. Man-in-the-middle (MITM)
attacks can compromise Internet of Things (IoT) devices in instances where
these devices engage in communication via networks that lack adequate se-
curity measures or encryption protocols.
–Remote code executionRemote code execution (RCE) attacks use vulnera-
bilities in software to facilitate the execution of malicious code on a device.
This technique enables the perpetration of harmful activities on the targeted
system. Internet of Things (IoT) devices may be susceptible to Remote Code
Execution (RCE) attacks when they utilize outdated or unpatched software.
–Theft of private information, Internet of Things (IoT) devices can collect
and store sensitive personal information such as passwords, bank account
numbers, and health records. Cybercriminals can exploit vulnerabilities in
these electronic devices to illicitly get sensitive information.
–Physical Attacks, In order to gain control over or extract data from an Inter-
net of Things (IoT) device, an attacker must first achieve physical proximity
to the device and subsequently implement modifications. In order to carry
out this sort of attack, the perpetrator must possess physical proximity to
the targeted device. However, it is noteworthy that detecting and mitigating
such attacks poses considerable challenges [5].
3 AI-based Solutions for IoT Cybersecurity
IoT technology has revolutionized how everyday objects communicate, leading to
a seamless interconnectivity as never before. One potential drawback associated
with a more prominent online population is the increased vulnerability of indi-
viduals to cybercriminal activities. The utilization of artificial intelligence (AI)
has the promise of substantially mitigating vulnerabilities inside the Internet of
Things (IoT).
Several AI-based precautions for the Internet of Things (IoT) are as follows:
–Anomaly DetectionIn scenarios where an Internet of Things (IoT) device
exhibits signs of compromise or deviates from expected behavior, artificial
intelligence (AI) can identify these anomalies and promptly notify the user
through an alarm system. Taking this step can help prevent potential cyber-
security threats in advance.
6 Iraq Ahmad Reshi and Sahil Sholla
–Predictive AnalyticsArtificial intelligence (AI) can analyze substantial vol-
umes of data derived from Internet of Things (IoT) devices and detect appar-
ent trends that could signify a breach in security. The utilization of predictive
analytics has the potential to facilitate the timely identification of potential
hazards.
–Dynamic Risk AssessmentThrough the surveillance of device activity and
identifying potential vulnerabilities, artificial intelligence (AI) can aid in an-
alyzing security threats associated with Internet of Things (IoT) devices in
a real-time manner. To ensure safety, it is important to promptly identify
and manage potential hazards.
–Dynamic Risk AssessmentArtificial intelligence can facilitate intelligence ac-
quisition on potential cyber-attacks targeted towards Internet of Things
(IoT) devices. This can aid enterprises in maintaining a competitive edge
in the dynamic threat landscape and safeguarding their Internet of Things
(IoT) infrastructure.
–Automatic Reaction Artificial intelligence (AI) can potentially optimize the
management of security events related to Internet of Things (IoT) devices.
Implementing this approach can enhance response times, mitigate the impact
of attacks, and bolster the overall defensive capabilities of the organization.
When implemented on a broad scale, artificial intelligence (AI) holds the po-
tential to provide a comprehensive set of tools for addressing cybersecurity
challenges in the Internet of Things (IoT) domain. It is important to note
that artificial intelligence (AI) should not be considered a universal solution
but rather a complementary tool that should be used with other protective
measures to mitigate cyberattacks successfully. The following section pro-
vides a concise overview of specific machine learning methods employed in
Internet of Things (IoT) security.
3.1 Machine learning algorithms for threat detection and response
Machine learning algorithms have demonstrated remarkable efficacy in threat
identification and response, owing to their ability to swiftly and accurately assess
vast amounts of data. Various types of machine learning algorithms, including
supervised learning, unsupervised learning, and reinforcement learning, are com-
monly utilized in this domain. Supervised learning methods can be employed to
train models in identifying distinct kinds of risks, leveraging annotated data.
An example of a supervised learning algorithm involves its training on a dataset
consisting of verified instances of phishing emails. Subsequently, this algorithm
can detect and classify new phishing attempts based on their resemblance to the
previously known examples. Unsupervised learning methods do not require prior
knowledge, making them advantageous for identifying patterns and anomalies
within extensive datasets. These algorithms possess significant use in threat de-
tection due to their capacity to find novel and unidentified dangers that may
have eluded identification using alternative methodologies. Reinforcement learn-
ing methods are employed in various threats and response domains, including
intrusion detection. These algorithms can undergo training to respond to distinct
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 7
categories of threats effectively, leveraging the feedback obtained from the system
over some time. An example of an application of reinforcement learning involves
training an algorithm to autonomously identify and isolate infected machines,
followed by the deployment of security patches as a response to a particular type
of malware. Furthermore, several specialized techniques and models, including
neural networks, decision trees, and support vector machines, are utilized in
the threat detection and response field in addition to the algorithms mentioned
earlier. The selection of an algorithm or technique is contingent upon several fac-
tors, including the characteristics of the identified threat, the data accessible for
analysis, and the specific goals of the implementing organization. The following
are many instances of frequently employed machine learning techniques utilized
in the domain of threat identification and response:
–Random Forestis a classification-centric supervised learning algorithm. Intru-
sion detection systems commonly identify malicious network traffic through
this technique. The technique creates numerous decision trees and aggregates
their outputs to ascertain a conclusive classification [8].
–K-means clustering Comparable data points are grouped using this unsu-
pervised learning approach. Identifying data points that significantly deviate
from the rest of the dataset allows for detecting anomalies. The utilization of
this approach holds potential in the identification and detection of previously
unidentified or novel threats [10].
–Deep learning, is a neural network commonly employed for intricate tasks
like image and speech recognition.Techniques like Deep packet inspection
utilized in threat detection, namely in analyzing network data to identify
malicious behavior originated from Deep Learning. [14].
–Support Vector Machines (SVMs) This algorithm for supervised learning is
used for classification tasks. It operates by locating a hyperplane that divides
various classes of data points. SVMs have been utilized for malware detection
by classifying samples as benign or malicious based on their characteristics
[19].
–Reinforcement Learning This algorithm acquires knowledge by reinforcing
positive behavior and penalizing poor behavior. The application of this ap-
proach has been observed in the context of threat response, which entails
the automated deployment of security patches or the isolation of infected
workstations as a reactive measure to detect threats [21].
These are just a handful of the countless machine learning algorithms and ap-
proaches used in threat identification and response. The selection of the algo-
rithm or technique to be employed will be contingent upon the characteristics of
the danger and the data at hand. Additionally, some intrusion prevention and
detection solutions are based on artificial intelligence.
3.2 AI-based intrusion detection and prevention
The use of machine learning algorithms for the detection and prevention of intru-
sions into Internet of Things (IoT) devices and networks is a relatively new area
8 Iraq Ahmad Reshi and Sahil Sholla
of study. Because there are so many different types of devices on an IoT network,
it’s very difficult for humans to keep track of all of the activities on the network
and see any signs of intrusion. The use of AI for spotting and stopping intrusions
is therefore crucial. Anomaly detection, signature-based detection, behavioural
analysis, and hybrid implementations are just some of the AI-based intrusion
detection and prevention strategies that can be used to the Internet of Things.
In order to detect potential attacks, these methods use machine learning algo-
rithms to examine typical network traffic, spot outliers, and compare incoming
traffic to a library of known attack signatures. Integrating AI-based intrusion de-
tection and prevention systems with preexisting security systems and protocols
can add a new degree of defence for the Internet of Things (IoT). As a whole,
AI-based intrusion detection and prevention is an encouraging step in protecting
IoT systems from vulnerabilities like data breaches, illegal access, and so on.
4 Blockchain-based Solutions for IoT Cybersecurity
While the IoT has greatly improved our experience with technological devices,
it has also introduced novel cybersecurity threats. The limited processing power
and storage capacity inherent in many IoT device designs make them ideal tar-
gets for cyber-attacks, which is a major problem. As a decentralised, immutable,
and secure architecture for IoT devices, blockchain technology has emerged as
another possible option to address these difficulties. Using blockchain in the In-
ternet of Things is significant since it ensures confidential data exchange between
gadgets. To prevent unauthorised changes or deletions, blockchain technology
uses a distributed ledger to record all financial transactions. So, it is more chal-
lenging for attackers to eavesdrop on data transmissions or alter the data in any
way. And because it eliminates the need for third parties or centrally controlled
agencies, blockchain technology makes data sharing among IoT devices much
safer [1]. Smart contracts, which are self-executing contracts where the condi-
tions of the agreements between the two parties are encoded directly into coding
lines, are another major advantage of blockchain in IoT. Smart contracts can
be used to remove the need for user intercession in transactions and exchanges
by executing them automatically and ensuring that only authorised devices can
communicate with each other. Also blockchain technology may be utilised to
manage Internet of Things devices in a safe and decentralised manner. Devices
can be registered, authenticated, and granted permission to communicate with
other devices by establishing a blockchain-based network. In this way, the net-
work is protected from intruders and only authorised devices are allowed access.
In addition, blockchain technology offers a safe, decentralised method of handling
software patches and upgrades, which lessens the likelihood of security flaws and
exploits. A critical example is depicted by figure 2 where medical IoT devices are
integrated with blockchain and smart contracts, to manage the healthcare data
of a patient suffering from diabetes. When applied to the security of Internet
of Things (IoT) devices, blockchain technology’s decentralised, immutable, and
secure architecture for communication, data exchange, and device management
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 9
Fig. 2. Diabetes management using IoT and Blockchain [3]
might significantly change the current approach. Blockchain-based solutions for
IoT cybersecurity are anticipated to play a crucial role in guaranteeing the se-
curity and privacy of our connected devices in the years to come, despite the
fact that there are still some problems to be addressed, such as scalability and
interoperability.
The authors in [9] provide a thorough examination of significant security con-
cerns in the realm of the Internet of Things (IoT), specifically focusing on the
layered architecture. This survey involves the classification and examination of
these difficulties, with a particular emphasis on networking, communication, and
management protocols. This study aims to provide a comprehensive analysis of
the fundamental security requirements for the Internet of Things (IoT), with a
particular focus on examining existing risks, attacks, and the latest responses
available. The research effectively compares and contrasts the recognised secu-
rity problems associated with the Internet of Things (IoT) with the solutions
that have been previously described in academic literature. The examination
of blockchain, the fundamental technology behind Bitcoin, holds great impor-
tance in addressing many security challenges that afflict the Internet of Things
(IoT). The authors provide valuable perspectives on emerging areas of research
and significant obstacles that need to be addressed in order to enhance the
security of the Internet of Things (IoT). A new and efficient blockchain (BC)-
based architecture specifically built for the Internet of Things (IoT) is proposed
in [6]. The framework aims to reduce the conventional overheads associated with
blockchain technology, while yet preserving most of its security and privacy ben-
efits. The suggested architectural design presents a centralised, yet confidential,
immutable ledger for Internet of Things (IoT) devices. This ledger is inspired
10 Iraq Ahmad Reshi and Sahil Sholla
by blockchain ideas and is specifically optimised to enhance energy efficiency. In
order to ensure total end-to-end security and privacy, a decentralised blockchain
is deployed using an overlay network on devices that possess sufficient power. By
utilising distributed trust methods, the processing time for block validation is
significantly decreased. The efficacy of this methodology is demonstrated within
the framework of a smart home environment, acting as a sample exemplifica-
tion for wider Internet of Things (IoT) implementations. The effectiveness of
the architecture in providing security and privacy for Internet of Things (IoT)
use cases is emphasised through qualitative evaluations conducted against exist-
ing threat models. In addition, simulations are used to verify the efficacy of the
suggested methodology, demonstrating significant decreases in packet transmis-
sion and processing overhead compared to the blockchain structure utilised in
Bitcoin. In this particular context, a resilient Proposed Application (PA) driven
by blockchain technology is envisioned with the objective of creating, maintain-
ing, and verifying healthcare certificates [18]. The PA functions as an inter-
mediary conduit, facilitating smooth communication between the foundational
blockchain infrastructure and key entities within the application ecosystem, in-
cluding hospitals, patients, doctors, and Internet of Things (IoT) devices. The
primary focus of its basic functionality lies in the generation and verification
of medical certifications. In addition, the Public Administration (PA) demon-
strates proficiency in implementing a variety of essential security measures, in-
cluding confidentiality, authentication, and access control. These measures are
effectively enforced through the integration of smart contracts. The effectiveness
of the suggested framework is shown through a rigorous comparative and perfor-
mance study, showcasing its superiority in comparison to existing alternatives.
Another paper presents a novel architecture for sharing IoT data, known as TEE-
and-Blockchain-supported IoT Data Sharing (TEBDS) [22]. TEBDS combines
on-chain and off-chain methods to effectively fulfil the security requirements of
the IoT data sharing framework. The TEBDS framework utilises a consortium
blockchain to ensure the security of on-chain Internet of Things (IoT) data and
manage access controls for IoT users. In addition to this, an introduction is made
to a Distributed Storage System (SDSS) that utilises Intel SGX technology in or-
der to enhance the security of off-chain data. In addition, a meticulously designed
incentive mechanism has been formulated to promote the smooth functioning of
the entire system. A comprehensive security analysis confirms that TEBDS ef-
fectively meets the requirements for ensuring both data security and identity
security. Empirical assessments provide evidence supporting the effectiveness of
TEBDS, demonstrating its improved performance compared to the centralised
SPDS strategy. Table 1, summarizes the works of blockchain for IoT security.
5 AI and Blockchain Integration for IoT Cybersecurity
The Internet of Things (IoT) could benefit from improved security measures if ar-
tificial intelligence (AI) and blockchain were coupled. Data collected by Internet
of Things (IoT) devices can be analysed by AI to reveal vulnerabilities. While
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 11
Table 1. Blockchain Solution for IoT
S.No Author &
Technique
Reference
1 Ali et al.
(2018)
propose a blockchain-based architecture for IoT
data security, enhancing confidentiality and in-
tegrity of data exchange, as well as device man-
agement
2 Khan et al.
(2018)
Blockchain technology’s advantage in enabling au-
tomated and secure smart contracts for IoT de-
vices is highlighted, along with its potential in
managing device registration, authentication, and
secure data sharing
3 Dorri et al.
(2017)
introduce a novel and energy-efficient blockchain
architecture for IoT devices, utilizing both cen-
tralized and decentralized approaches to enhance
security and privacy
4 Sharma et al.
(2023)
present a proposed application (PA) utilizing
blockchain to generate and verify healthcare cer-
tificates, incorporating robust security measures
through smart contracts
5 Xie et al.
(2023)
introduce TEBDS, a hybrid on-chain and off-chain
architecture utilizing consortium blockchain and
Intel SGX technology to secure IoT data sharing
traditional methods of data storage and dissemination have their limitations,
blockchain technology offers a safe, distributed alternative. Anomaly detection
is one method in which AI can strengthen the security of the Internet of Things.
Algorithms powered by AI may be taught to analyse data for anomalies that
can indicate a breach in security. This can be especially helpful in preventing
and responding to attacks that leverage Internet of Things (IoT) devices as vec-
tors into bigger networks. Blockchain technology has the potential to serve as
a trustworthy, decentralised database for IoT information. Blockchain’s use of a
distributed ledger system makes data immutable and resistant to hacking. This
can safeguard information collected by IoT devices and lessen the likelihood of
data breaches. Together, AI and blockchain can strengthen the IoT ecosystem’s
defences and make it more resistant to disruption. Artificial intelligence (AI)
can be used to detect security risks, setting off automated responses that em-
ploy blockchain technology to protect and verify the integrity of the relevant
data. This can aid in protecting the IoT from threats and guaranteeing that
only authorised parties have access to its data. Artificial intelligence (AI) and
blockchain technology (blockchain) could also be used for identity management
purposes in IoT cybersecurity. Artificial intelligence can examine patterns of use
and flag outliers that may suggest intrusion. To further ensure that only au-
thorised users have access to IoT devices and data, blockchain can be used to
securely store user IDs and authentication data. Several other applications exist
for combining AI and blockchain technology in IoT security,
12 Iraq Ahmad Reshi and Sahil Sholla
–Fraud detection, For the purpose of detecting fraudulent behaviours, such as
data manipulation or illegal access, artificial intelligence algorithms can be
applied to IoT data in real time. The immutable recordings of these actions
can then be stored in blockchain, providing a safe and verifiable audit trail.
–Threat intelligence sharing,To better understand cyberattack patterns and
trends, threat intelligence data from many sources can be analysed by AI.
Secure data sharing via a blockchain-based platform makes it possible for
businesses to work together to combat security concerns.
–Device authentication,To verify the legitimacy of a device, we can utilise ar-
tificial intelligence to monitor its activity for any deviations that would point
to the presence of a malicious piece of hardware. The device’s authenticity
can then be verified via blockchain, allowing only approved gadgets access
to the network.
–Safety of smart contract, Smart contracts are blockchain-stored, automatically-
executing contracts, commonly used to interface IoT in Blockchain. Artificial
intelligence (AI) can inspect these agreements for loopholes and other secu-
rity problems. This can protect smart contracts from attacks that take use
of security flaws.
–Smart contract security,With AI and blockchain technology, supply chains
may be made more secure and transparent by keeping track of items as
they flow from supplier to customer. This has the potential to deter forgery,
tampering, and other fraudulent activities.
When combined, AI and blockchain have the potential to greatly strengthen the
reliability and safety of IoT infrastructure. The Internet of Things (IoT) ecosys-
tem can be made safer from cyberattacks by integrating these two technologies.
5.1 State-of-art Survey of Combination of AI and Blockchain for
IoT Security
Integrating Blockchain and AI facilitates the resolution of issues about accuracy,
latency, centralization, and, most importantly, security in IoT architectures. The
database within blockchain technology is responsible for storing transactions ac-
companied by a digitally signed hash value. Hence, it is imperative to address
accuracy, latency, security, privacy, and centralization concerns to mitigate po-
tential challenges. Artificial intelligence (AI) algorithms are also employed to
address these challenges. Utilizing blockchain networks offers the advantage of
decentralization, which facilitates automatic and efficient data validation. This
feature effectively addresses the issue of single points of failure that commonly
arise in cloud servers utilized for extensive data analysis. Furthermore, integrat-
ing blockchain technology with the Internet of Things (IoT) and artificial intelli-
gence (AI) provides further benefits in enhancing the capabilities of IoT systems.
Rathore et al. in [15] presents a security architecture for Internet of Things (IoT)
networks. The architecture aims to ensure the secure and scalable transmission
of IoT data from decentralized IoT applications at the fog layer. Artificial intel-
ligence (AI) is employed in diverse domains of advanced technologies, including
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 13
blockchain thinking , decentralized AI , the intelligence of things, and intelligent
robots, among others, in the daily lives of individuals [16]. The convergence be-
tween artificial intelligence (AI) Internet of Things (IoT) enables the collection
of a vast amount of data and facilitates its analysis. Machine learning is utilized
in various domains, including healthcare, smart home technology, smart farming,
and intelligent vehicles, among others, to facilitate effective learning processes.
Rathore et al. [15]introduced a novel approach that utilizes blockchain tech-
nology to enhance the security of deep learning in the context of Internet of
Things (IoT) applications. By integrating Blockchain and artificial intelligence
(AI) at the device layer, their suggested method aims to ensure the integrity
and reliability of data in IoT systems. The system demonstrates a significant
level of precision and a notable delay in processing time for Internet of Things
(IoT) data. In their study, Gil et al. [7] examined the role of intelligent machines
in several domains, including medical science, automatic sensing devices, auto-
mated vehicle driving, and cooking, to reduce human labor. Intelligence can be
defined as the cognitive capacity to use acquired knowledge to address intricate
challenges. In contrast, artificial intelligence (AI) refers to the learning approach
that facilitates the development of innovative procedures and the dissemina-
tion of collected initial insights. A report by McKinsey [4]projected that the AI
market will experience significant growth, reaching a value of 13 trillion US dol-
lars by 2030. The decentralized AI approach is a fusion of artificial intelligence
(AI) and blockchain technology. Its purpose is to facilitate secure and trust-
worthy information sharing without relying on intermediaries. This is achieved
through the utilization of cryptographic signatures and robust security mea-
sures. Furthermore, it can autonomously make decisions in Internet of Things
(IoT) applications. In recent years, the rapid evolution of technologies, devices,
and Internet of Things (IoT) devices has resulted in Blockchain, Artificial In-
telligence (AI), and IoT emerging as the most influential technologies, driving
the acceleration of innovative ideas across several domains. Researchers in [20]
examines privacy, accuracy, latency, and centralization concerns in integrating
Blockchain and AI technologies within Internet of Things (IoT) applications.
Blockchain and AI are integrated to propose an Intelligent IoT Architecture
incorporating Blockchain technology, named as blockiotintelligence. This archi-
tecture consists of a decentralized cloud infrastructure enabled by Blockchain at
the cloud layer, distributed fog networks based on Blockchain at the fog layer,
distributed edge networks based on Blockchain at the edge layer, and the conver-
gence of peer-to-peer Blockchain networks at the device layer. Study by authors
in [17] introduces a novel security model called the Artificial Intelligence-based
Lightweight Blockchain Security Model (AILBSM) to improve privacy and secu-
rity in Industrial Internet of Things (IIoT) systems based on cloud computing.
The framework utilizes a combination of lightweight blockchain technology and
a Convivial Optimized Sprinter Neural Network (COSNN) based AI mechanism.
This integration enables the framework to deploy an Authentic Intrinsic Analy-
sis (AIA) model, effectively converting features into encoded data. As a result,
the framework mitigates the potential impact of attacks. By conducting exten-
14 Iraq Ahmad Reshi and Sahil Sholla
sive experiments and assessments, the AILBSM framework demonstrates notable
outcomes, such as a decreased execution time of 0.6 seconds, an improved clas-
sification accuracy of 99.8%, and an enhanced detection performance of 99.7%.
The superior anomaly detection capability of the model can be attributed to
its new features, such as auto-encoder-based transformation and blockchain au-
thentication. This represents a noteworthy improvement in Industrial Internet
of Things (IoT) security, distinguishing it from other existing methodologies.
Another study in [23] presents a novel framework for cloud-edge-end architec-
ture that utilizes blockchain technology as a foundation for a trust mechanism
based on the principles of blockchain consensus. The system utilizes a consensus
approach known as BLS-based proof of replication (PoRep) to establish con-
fidence among devices. The data from devices is securely maintained within
servers, utilizing mechanisms like proof generation and consensus establishment
by broadcasting information by nodes. This methodology improves the level of
network data security and fosters the establishment of trust across a wide range
of devices. In addition, the solution integrates verifiable delay functions (VDF)
based on secret sharing to mitigate the need for servers to perform dynamic
data replication calculations in Proof of Replication (PoRep), enhancing con-
sensus efficiency among nodes. Implementing this complete strategy represents
a notable progression in establishing secure and reliable interactions inside the
network. The authors in [13] introduced a highly advanced intrusion detection
system designed for smart cities. The system consists of three essential modules:
–a trust module incorporating a reputation system based on blockchain tech-
nology and addresses
–a privacy module with two tiers employing an improved Proof of Work tech-
nique based on blockchain
–an intrusion detection module
This study presents an integrated infrastructure called Cloud-Block, Fog-
Block, and Edge-Block, explicitly designed for smart city environments. This
infrastructure aims to optimize the Edge-Fog-Cloud architecture by capitaliz-
ing on its advantages and mitigating its constraints. The system’s effective-
ness is demonstrated through the evaluation conducted on the sBoT-IoT and
TON-IoT datasets. A comparative comparison with other state-of-the-art sys-
tems shows that the suggested system surpasses its equivalents, emphasizing its
notable progress in the field of smart city cybersecurity. Furthermore, the study
in [2] explores the intricacies of reconciling various technologies and suggests
practical solutions to address these challenges. It provides a complete analy-
sis of the prospects of this amalgamation, emphasizing its potential to bring
about significant changes in various areas, including banking, healthcare, and
transportation. The study also investigates existing models’ pre-integration lim-
itations, including problems such as lack of transparency, concerns over data
privacy and security, and deficiencies in automation. The comprehensive ex-
amination of the integration of blockchain, artificial intelligence, and IoT not
only highlights their promise but also recognizes the significant challenges that
prompted this convergence.Table 1 Summarizes the literature.
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 15
Table 2. Summary of state-of-art Proposed Solutions
S.No. Year Author Main Theme of Paper
1 2016 [7] Role of intelligent machines across domains, defining intelligence
and AI’s role in developing innovative procedures.
2 2018 [15] Security architecture for IoT networks, utilizing AI and
blockchain to ensure secure and scalable IoT data transmission.
3 2020 [20] Privacy, accuracy, latency concerns in integrating Blockchain
and AI, proposing an Intelligent IoT Architecture incorporating
Blockchain.
4 2023 [17] Introduction of an AI-based Lightweight Blockchain Security
Model (AILBSM) for IIoT security enhancement, utilizing
blockchain and AI mechanisms.
5 2023 [23] Novel cloud-edge-end architecture using blockchain-based trust
mechanism, integrating BLS-based PoRep consensus, enhancing
network security and trust.
6 2023 [13] Intrusion detection system for smart cities using blockchain-
based trust, privacy, and intrusion modules, optimizing smart
city cybersecurity.
7 2023 [2] Exploration of integrating blockchain, AI, and IoT, discussing
potential changes and challenges in various domains.
5.2 Challenges and limitations of AI and blockchain integration for
IoT security
Integrating AI with blockchain for IoT security offers the ability to address some
of the most pressing security issues with the IoT, including data breaches, device
tampering, and unauthorised access. There are, however, a number of problems
and restrictions with this integration, as well as some research areas that should
be covered up.
–Scalability One of the challenges encountered in the implementation of IoT
security through the use of AI and blockchain is the issue of scalability. The
volume of data generated by Internet of Things (IoT) devices is substantial,
necessitating significant computational resources for analysis. Integrating ar-
tificial intelligence (AI) and blockchain technology may result in a substantial
increase in processing requirements, thus impeding the system’s scalability.
–Cost The cost The integration of artificial intelligence (AI) and blockchain
technology has the potential to enhance the security of the Internet of Things
(IoT). However, this advancement has its associated costs. Both blockchain
technology and AI algorithms require significant computational resources.
Consequently, the cost associated with implementing this system may in-
crease, rendering it financially unattainable for many smaller enterprises.
–Complexity Integrating artificial intelligence (AI) and blockchain technol-
ogy to enhance security measures for Internet of Things (IoT) devices may
present specific challenges. Integrating both systems necessitates proficiently
understanding their potential applications in safeguarding Internet of Things
16 Iraq Ahmad Reshi and Sahil Sholla
(IoT) devices. Implementing this system may pose challenges for certain
businesses due to its inherent complexity.
–Interoperability Interoperability is the ability of different systems or compo-
nents to exchange and use information. Interoperability poses an additional
challenge in utilizing artificial intelligence (AI) and blockchain technologies
to enhance security in the Internet of Things (IoT). Integrating diverse IoT
devices into a unified system can present difficulties when these devices op-
erate on disparate protocols and standards.
5.3 Research Gaps
In order to improve interoperability and compatibility across devices and sys-
tems, standardization is necessary for the integration of AI and blockchain for
IoT security. To further ensure the security of personal information submitted
by users retained on IoT devices, privacy-preserving algorithms must be devel-
oped. In addition to integrity models, trust models are crucial to the security of
the system. In addition, ensuring the IoT system and its data are secure from
cyberattackers relies heavily on the governance of AI and blockchain integra-
tion. Despite the possible advantages, there are also restrictions and difficulties
to overcome, such as scalability, complexity, and expense. Improving the efficacy
and safety of AI and blockchain integration for IoT security requires addressing
research gaps in areas like standardization, privacy, trust, and regulations.
6 Case Studies of AI and Blockchain-based IoT
Cybersecurity Systems
In this section, we will brief about few usecases in the field of AI, IoT and
blockchain, and how one technology can benefit the other.
Case study 1
AI-based threat detection in industrial IoT: A manufacturing facility could be
one use case for AI-based threat detection in the industrial Internet of Things.
Various instruments and devices in a typical manufacturing facility are network-
connected and communicate data with one another and the central control
system. This network of devices, known as the Industrial Internet of Things
(IIoT), is responsible for maintaining the efficiency of the manufacturing pro-
cess. Nonetheless, the IIoT is a primary target for cyber attacks, and any network
disruption can result in production delays, outages, and financial losses. Here,
AI-based threat detection can be helpful.Using machine learning algorithms to
analyse network traffic, AI can identify peculiar patterns and anomalies that
may be indicative of a cyber attack. AI can detect, for instance, if a machine
sends and receives data at an abnormal rate or if it communicates with an unau-
thorised device. Once a threat has been identified, the AI system can mitigate
it by alerting the security team, blocking suspicious traffic, or closing down the
afflicted machine. In addition, AI-based threat detection can assist with coun-
termeasures, such as detecting network vulnerabilities and identifying potential
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 17
weak spots before they are exploited by cyber attackers. Overall, AI-based threat
detection in industrial IoT can aid in ensuring the safety and security of the man-
ufacturing facility, preventing cyber attacks and production process disruptions,
and saving the organization time and money.
Case study 2
Blockchain-based secure data sharing in healthcare IoT: Another Use case can be
a, Blockchain-based secure data sharing in healthcare IoT wishing to exchange
patient information with other facilities in a safe and reliable manner. Shar-
ing patient data across healthcare providers has historically been a challenging
and tedious procedure due to the need to safeguard the data’s accuracy, secu-
rity, and compliance with standards like HIPAA and GDPR. To remedy this,
we may utilize blockchain technology to safely share information. Healthcare
providers may build a trustworthy, distributed network for exchanging patient
information using blockchain technology. The blockchain stores an immutable
and comprehensive record of patient information, and each healthcare practi-
tioner may keep their own copy. The blockchain network may be queried by
healthcare providers whenever they need to access patient data, at which point
the network will confirm the provider’s identity and provide them access. Since
the data is maintained in a decentralised and tamper-proof way, it protects the
privacy of patients and allows for the free flow of information among healthcare
providers without compromising any of the data’s integrity. Data interoperabil-
ity can be aided by blockchain-based secure data sharing since it lets healthcare
professionals exchange patient information between disparate systems and plat-
forms without jeopardising patient privacy or security. This has the potential to
enhance patient outcomes, cut down on wasted resources, and lower healthcare
costs. Overall, blockchain-based safe data sharing in healthcare IoT can aid in
protecting patients’ privacy, keeping their data private, and facilitating the se-
cure transfer of data across various medical facilities.
Case study 3
AI and blockchain-based identity management in smart homes: A smart apart-
ment complex is another potential use of artificial intelligence and blockchain-
based identity management in the context of smart housing. Thermostats, cam-
eras, and house assistants are just some of the internet-connected equipment that
may be remotely managed in a typical ”smart home.” This, however, makes them
susceptible to cyber assaults and unauthorised access, which in turn threatens
the inhabitants’ privacy and safety. Artificial intelligence and identity manage-
ment using blockchain technology can assist here. AI may monitor the locals’
actions for signs of suspicious or out-of-the-ordinary behaviour using machine
learning methods. A cyber assault may be in progress if, for instance, a smart
thermostat is repeatedly toggled on and off in a short amount of time. A de-
centralized and secure network for managing identification and access control
may be built using blockchain technology to protect people’ privacy and safety.
Every citizen has the option of keeping their digital identity on the blockchain,
which stores an immutable record of their personal information and permits.
When a user needs to access a smart home device, they may do so by sending
18 Iraq Ahmad Reshi and Sahil Sholla
a request to the blockchain network, which will then confirm the user’s identity
and provide them access. Because the information is kept in a distributed and
unalterable fashion, residents may be certain that their personal information
will remain private. Moreover, AI and blockchain-based identity management
may aid in preventative measures like detecting weaknesses in the smart home
network and pinpointing possible points of attack in advance. Overall, identity
management in smart homes based on AI and blockchain technology can help
protect the personal information of residents and keep their smart home gadgets
accessible in a safe and reliable manner.
7 Conclusion
This chapter has presented an overview of the cybersecurity threats encountered
by the Internet of Things (IoT) and the potential solutions offered by artificial
intelligence (AI) and blockchain technologies. Recent vulnerabilities and cyber
threats have emerged as a result of the growing number of connected devices in
the IoT ecosystem, and they may be beyond the capabilities of more conventional
security measures. IoT cybersecurity may be made more effective and efficient
with the use of AI-based solutions like machine learning algorithms for threat
detection and response and intrusion detection and prevention systems. Simi-
larly, blockchain technology enables a decentralized and immutable ledger for
IoT devices, which improves data privacy, integrity, and resilience to assaults.
Case studies given in this chapter illustrate the actual uses and benefits of in-
tegrating AI and blockchain technology to further increase the security of IoT
settings. There are, however, obstacles and restrictions to combining AI and
blockchain for IoT safety. The requirement for standardization, scalability, and
interoperability are all examples. It is crucial to tackle these issues and provide
solid solutions that can properly safeguard these systems as the IoT ecosystem
continues to grow. In conclusion, this chapter has demonstrated the great poten-
tial of AI and blockchain technology in the context of IoT cybersecurity. Better
threat detection and response, more secure data storage, and a more secure In-
ternet of Things (IoT) may all be attained through the use of these technologies.
References
1. Ali, M.S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., Rehmani, M.H.:
Applications of blockchains in the internet of things: A comprehensive survey.
IEEE Communications Surveys & Tutorials 21(2), 1676–1717 (2018)
2. Aruna, S., Priya, S.M., Reshmeetha, K., Sudhayini, E.S., Narayanan, A.A.:
Blockchain integration with artificial intelligence and internet of things technolo-
gies. In: 2023 7th International Conference on Intelligent Computing and Control
Systems (ICICCS). pp. 688–694. IEEE (2023)
3. Azbeg, K., Ouchetto, O., Andaloussi, S.J., Fetjah, L., Sekkaki, A.: Blockchain
and iot for security and privacy: a platform for diabetes self-management. In:
2018 4th international conference on cloud computing technologies and applications
(Cloudtech). pp. 1–5. IEEE (2018)
Leveraging AI and Blockchain for Enhanced IoT Cybersecurity 19
4. Chui, M., et al.: The economic potential of generative ai: The next productivity
frontier. McKinsey & Company (2023), (Accessed on 12 August 2023)
5. Deogirikar, J., Vidhate, A.: Security attacks in iot: A survey. In: 2017 International
Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). pp.
32–37. IEEE (2017)
6. Dorri, A., Kanhere, S.S., Jurdak, R.: Towards an optimized blockchain for iot. In:
Proceedings of the second international conference on Internet-of-Things design
and implementation. pp. 173–178 (2017)
7. Gil, D., Ferr´andez, A., Mora-Mora, H., Peral, J.: Internet of things: A review of
surveys based on context aware intelligent services. Sensors 16(7), 1069 (2016)
8. Islam, N., Farhin, F., Sultana, I., Kaiser, M.S., Rahman, M.S., Mahmud, M., Hosen,
A., Cho, G.H.: Towards machine learning based intrusion detection in iot networks.
Comput. Mater. Contin 69(2), 1801–1821 (2021)
9. Khan, M.A., Salah, K.: Iot security: Review, blockchain solutions, and open chal-
lenges. Future generation computer systems 82, 395–411 (2018)
10. Liu, C., Gu, Z., Wang, J.: A hybrid intrusion detection system based on scalable
k-means+ random forest and deep learning. Ieee Access 9, 75729–75740 (2021)
11. Lu, Y., Da Xu, L.: Internet of things (iot) cybersecurity research: A review of
current research topics. IEEE Internet of Things Journal 6(2), 2103–2115 (2018)
12. Maple, C.: Security and privacy in the internet of things. Journal of cyber policy
2(2), 155–184 (2017)
13. Moawad, M.M., Madbouly, M.M., Guirguis, S.K.: Leveraging blockchain and ma-
chine learning to improve iot security for smart cities. In: The International Confer-
ence on Artificial Intelligence and Computer Vision. pp. 216–228. Springer (2023)
14. Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for iot
big data and streaming analytics: A survey. IEEE Communications Surveys &
Tutorials 20(4), 2923–2960 (2018)
15. Rathore, S., Park, J.H.: Semi-supervised learning based distributed attack detec-
tion framework for iot. Applied Soft Computing 72, 79–89 (2018)
16. Reshi, I.A., Sholla, S.: Challenges for security in iot, emerging solutions, and re-
search directions. International Journal of Computing and Digital Systems 12(1),
1231–1241 (2022)
17. Selvarajan, S., Srivastava, G., Khadidos, A.O., Khadidos, A.O., Baza, M., Alshehri,
A., Lin, J.C.W.: An artificial intelligence lightweight blockchain security model for
security and privacy in iiot systems. Journal of Cloud Computing 12(1), 38 (2023)
18. Sharma, P., Namasudra, S., Crespo, R.G., Parra-Fuente, J., Trivedi, M.C.: Ehdhe:
Enhancing security of healthcare documents in iot-enabled digital healthcare
ecosystems using blockchain. Information Sciences 629, 703–718 (2023)
19. Shen, M., Tang, X., Zhu, L., Du, X., Guizani, M.: Privacy-preserving support
vector machine training over blockchain-based encrypted iot data in smart cities.
IEEE Internet of Things Journal 6(5), 7702–7712 (2019)
20. Singh, S.K., Rathore, S., Park, J.H.: Blockiotintelligence: A blockchain-enabled
intelligent iot architecture with artificial intelligence. Future Generation Computer
Systems 110, 721–743 (2020)
21. Uprety, A., Rawat, D.B.: Reinforcement learning for iot security: A comprehensive
survey. IEEE Internet of Things Journal 8(11), 8693–8706 (2020)
22. Xie, H., Zheng, J., He, T., Wei, S., Hu, C.: Tebds: A trusted execution environment-
and-blockchain-supported iot data sharing system. Future Generation Computer
Systems 140, 321–330 (2023)
20 Iraq Ahmad Reshi and Sahil Sholla
23. Zhang, F., Wang, H., Zhou, L., Xu, D., Liu, L.: A blockchain-based security and
trust mechanism for ai-enabled iiot systems. Future Generation Computer Systems
146, 78–85 (2023)
