Addressing IoT Security Challenges through AI Solutions

International Journal of Computer Applications (0975 – 8887)
Volume 186 – No.45, October 2024
50
Addressing IoT Security Challenges through AI
Solutions
Md Shihab Uddin
School of Engineering
San Francisco Bay University
Fremont, CA 94539
ABSTRACT
The Internet of Things (IoT) is a widely recognized technology
that profoundly influences various sectors, including
connectivity, work, healthcare, and the economy. IoT holds the
potential to enhance daily life across different environments,
from smart cities to educational settings, by automating
processes, boosting efficiency, and reducing stress. However,
the rise of cyberattacks and threats poses significant challenges
to the security of intelligent IoT applications. Traditional
methods for securing IoT are becoming increasingly ineffective
due to emerging threats and vulnerabilities. To maintain robust
security protocols, future IoT systems will require the
integration of AI-powered machine learning and deep learning
techniques. Leveraging the capabilities of artificial
intelligence, particularly through machine learning and deep
learning, is essential for equipping next-generation IoT systems
with dynamic and adaptive security mechanisms. This paper
explores IoT security intelligence from various perspectives,
proposing an innovative approach that utilizes machine
learning and deep learning to extract insights from raw data,
thereby protecting IoT devices against a wide range of
cyberattacks. It also discusses how these technologies can be
employed to detect attack patterns in unstructured data and
enhance the security of IoT devices.
Keywords
Internet of things; cyberattacks; anomalies; deep learning;
machine learning; security; data security; network security
1. INTRODUCTION
The Internet of Things (IoT) paradigm has gained popularity in
recent years. At a conceptual level, IoT refers to the inter
connectivity among our everyday devices, along with device
autonomy, sensing capability, and contextual awareness. IoT
devices include personal computers, laptops, tablets, smart
phones, PDAs, and other hand-held embedded devices.
Devices now communicate smartly to each other or to us.
Connected devices equipped with sensors and/or actuators
perceive their surroundings, understand what is going on and
perform accordingly [1] [2]. This is achieved by processing the
sensed data at a node, device hub, or in a cloud. Devices are
also enabled to take decisions autonomously or may propagate
information to users, so that users can make the best decisions
[2].
The interconnected device networks can lead to a large number
of intelligent and autonomous applications and services that
can bring significant personal, professional, and economic
benefits [3. 4], resulting in the emergence of more data-centric
businesses. IoT devices have to make their data accessible to
interested parties, which can be web services, smart phone,
cloud resource, etc.
2. LITERATURE REVIEW
The Internet of Things (IoT) is a key player in technological
progress. The term "IoT" refers to the "Internet of Things,"
where "Things" are electronic devices connected to the internet.
The Fourth Industrial Revolution, or Industry 4.0, is
characterized by the increased automation of traditional
industrial and manufacturing processes, with IoT being one of
the advanced technologies driving this movement [11].
IoT refers to a network of objects that can connect to the
internet and wireless networks to exchange data automatically.
Various organizations and research groups define the IoT and
smart environments from different perspectives. According to
the authors, the IoT is composed of RFID-based digital
information flows and physical components [12].
In the healthcare sector, the IoT is rapidly being adopted,
offering the potential to enhance patient engagement, health
outcomes, and access to care. However, the proliferation of IoT
devices also introduces significant security, privacy, and safety
risks for both patients and healthcare providers. Despite this,
there is still limited research focused on mitigating the risks
posed by IoT in healthcare. Recent studies have explored
integrating secure application solutions with IoT devices in
healthcare settings. Due to the sensitive nature of healthcare
data, developing a specialized IoT application for healthcare is
critical [13]. Current IoT opportunities in the healthcare
industry are promising, particularly because of their sensing
and measurement capabilities, including the low-energy variant
of narrowband IoT (N.B. IoT). Due to its low energy
consumption, N.B. IoT is favored in the healthcare sector.
There are various concepts for utilizing N.B. IoT in healthcare,
and it has gained popularity for its compatibility with cellular
systems such as LTE. Consequently, N.B. IoT has become a
viable option for healthcare-related applications in recent years.
However, the primary challenges for N.B. IoT involve security
measures and other system-related issues. If these challenges
are adequately addressed, N.B. IoT could become one of the
most feasible and widely adopted solutions for low-power,
wide-area healthcare installations [14].
One of the many challenges faced by the Internet of Things,
which connects a wide array of objects to networks to enable
complex and intelligent applications, is ensuring user privacy
and protecting against various attacks, such as spoofing, denial
of service (DoS), jamming, and eavesdropping. The author
explores the vulnerabilities within IoT systems and potential
methods for securing IoT networks using machine learning
techniques, including supervised learning, unsupervised
learning, and reinforcement learning (RL). The analysis of data
privacy emphasizes ML-based approaches for authenticating
IoT devices, controlling access, securely offloading data, and
detecting malware. The future adoption of IoT will have a
substantial impact on society, business, and the economy. Since

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most nodes in an IoT network have limited resources, they are
often targeted by hackers. Various solutions have been
proposed to address IoT network security and privacy
concerns, most of which rely on standard cryptographic
protocols. However, these existing solutions are insufficient for
addressing the unique security challenges posed by IoT
networks. By incorporating machine learning (ML) and deep
learning into IoT devices and networks, many threats to IoT
security can be mitigated.
The current IoT opportunities in the healthcare sector are
promising, especially due to its sensing and measuring
capabilities, including the low-energy form of narrowband IoT.
This technology is popular in the healthcare field because of its
low energy usage. Several concepts exist for using narrowband
IoT in the healthcare industry. Narrowband IoT has become
widely used and is compatible with cellular networks like LTE.
As a result, narrowband IoT has recently emerged as a viable
choice for healthcare-related applications [15]. The IoT risk
management model in healthcare is presented in Figure 1.
Figure 1: The IoT risk management model in healthcare
[16].
.
Figure 2: Potential economic impact of dominant IoT
applications by 2025 [22]
3. SECURITY ISSUES IN THE SENSING
LAYER
A typical IoT architecture is composed of three main layers: the
application layer, the network layer, and the perception layer
[23]. However, as the importance of data processing and
intelligent decision-making continues to grow, the support or
middleware layer between the network and application layers
has become increasingly crucial. IoT systems can include
multiple layers, such as a network layer and a support layer. In
many studies on IoT systems, cloud computing has been
utilized as the underlying support layer.
The perception layer, also known as the sensing layer,
comprises various sensors and devices. This layer is
characterized by limited storage, processing, memory, and
communication capabilities. The primary security measures in
this layer include node authentication, weak encryption, and
access control [24]. Unfortunately, privacy attacks and crimes
targeting the perception layer are prevalent in real-world
scenarios. One common attack method involves taking control
of a node. Other techniques include the use of malicious code,
data injection, replay attacks, and side-channel attacks. For
instance, if an attacker gains control of a node, it may cease to
transmit valid network data and could even disable the IoT
security software. If the IoT application receives corrupted data
or is compromised by malicious code injection, it may not
function as intended. Eavesdropping, also known as sniffing or
snooping, is a technique where an attacker intercepts and
examines data exchanged between two devices [25]. In an IoT
network, a replay attack involves repeatedly falsifying,
altering, or reusing the identities of related objects. Given
sufficient time and access to encryption keys, an attacker can
also perform a timing attack. These are just a few examples of
the numerous ways vital information can be compromised
beyond direct node attacks [26].
Table 1: IoT Key Issues
Ref. IoT key issues Advantages
[34] Quality of service
(QoS)
Involves managing data
traffic load and protocols
across all layers in IoT
architecture, including
routine checks for QoS
(Quality of Service) and QoE
(Quality of Experience).
[35] Authentication
and identification
Focuses on addressing issues
related to IoT integrations
with internet protocols
(IPv6), as well as
authentication and
identification issues.
[36] Environment,
power, and energy
Discusses the incorporation
of green technology in IoT,
the design of low-power-
consumption devices and
chips, and aspects of
pollution control and
management.
[37] Reliability Covers connectivity,
mobility, and routing issues,
along with the reliability of
infrastructure and IoT
applications.
[38] Scalability Discusses the challenges of
scaling IoT solutions across
large platforms and
geographical locations,
including potential discovery
services.
4. SECURITY ISSUES IN THE
MIDDLEWARE OR SUPPORT LAYER
Distributed computing solutions have been used to replace
centralized cloud environments in a variety of cases, with good
results in terms of performance and response time. All sent data
should now be checked for accuracy, concision, and secrecy.
When someone inside a network purposefully alters or steals
data or information, this is known as a malicious inside attack

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[29]. By inserting malicious SQL queries into the code, SQL
injection attacks are used to steal data from user services in the
real world. When damage to one virtual machine spreads to
another, this is a virtualization attack. With the help of cloud
malware injection, a hacker can take over a cloud service,
install malicious code, or even create a fake virtual machine.
There could be significant consequences if attacks are so
powerful that cloud infrastructure is incredibly frustrated [30].
5. APPLICATION LAYER
Defining and maintaining IoT applications, including their
interactions with specific clients, fall under the scope of the
application layer. One way to use IoT services is through a user
interface. A computer, a smartphone, or any other Internet-
enabled smart device could serve as an interface. The data that
the middleware layer process is used by the application layer
[31]. This holds for a wide range of application categories,
including applications for smart homes, smart cities, industry,
construction, and health. The security needs of an application
may change depending on how it functions. When sending
information on climate change forecasts as opposed to when
conducting online banking, it is acceptable to expect a better
level of security. The application layer must address various
security challenges, such as attacks on access control,
malicious code, programming, data leaks, service interruptions,
application vulnerabilities, and software flaws [32]. Attacks
that interrupt service, commonly referred to as “Distributed
Denial of Service (DoS)” attacks, stop users from using IoT
apps by sending a flood of requests to servers or networks.
Threat actors could use sniffer software to monitor data being
transmitted by IoT apps. Attacks that gain unauthorized access
can seriously harm a system quickly by preventing users from
using IoT-related services and wiping data [33-36].
6. IoT SECURITY SOLUTIONS BASED
ON ML
ML is transforming IoT security by enabling real-time
monitoring of network traffic to detect anomalies that may
indicate security breaches. ML and DL models are also used for
UAV detection [37, 38]. These models continuously analyze
data patterns from connected devices, identifying deviations
from typical behavior that could signal a cyber threat. This
section reviews current trends in IoT security, presenting
specific examples and case studies that illustrate how ML
methodologies are applied in practical scenarios.
6.1 Anomaly Detection Systems
ML models are employed in IoT networks for anomaly
detection, where they monitor network traffic in real-time to
spot unusual patterns indicative of security breaches. These
models are unique in their ability to learn and adapt, using
historical data to recognize new and emerging threats [39, 40].
This adaptability ensures that security measures evolve in
tandem with the changing nature of cyber attacks, enhancing
the capability to preemptively identify potential threats and
customize security protocols to the specific characteristics of
each IoT network. This approach provides a more robust and
responsive defense mechanism against a broad spectrum of
cyber threats [41].
6.2 Predictive Maintenance in Industrial
IoT
In modern industrial environments, the use of ML algorithms
for the predictive maintenance of IoT devices has become
increasingly important. This proactive maintenance strategy
leverages the vast amounts of sensor data collected by IoT
devices to predict and prevent equipment failures before they
happen. IoT devices in industrial settings are equipped with
various sensors that constantly monitor and gather data on the
performance and condition of machinery, including parameters
like temperature, vibration, and pressure. ML algorithms
analyze this extensive data to detect patterns and anomalies that
could indicate potential failures or malfunctions [42].
6.3 Smart Home Security Systems
In the rapidly advancing consumer market, ML has become a
crucial technology for enhancing the security of smart home
devices. The integration of ML into home security systems is
revolutionizing the management of security in residential
spaces. One of the primary applications of ML in this area is
through advanced facial recognition technologies. Unlike
traditional security systems that rely on static passcodes or
keys, ML-powered systems can dynamically recognize the
faces of residents and frequent visitors, offering a more
personalized and secure experience. These systems
continuously learn and improve their accuracy over time by
analyzing the various faces they encounter. Additionally, smart
security systems can learn and understand regular household
patterns and routines, enabling them to detect anomalies or
unusual activities, such as movement in an empty house or a
door being opened at an odd hour. This feature is particularly
useful for monitoring elderly family members or securing the
home while away [43].
6.4 Automotive Security
Connected vehicles represent a major advancement in the
automotive industry, integrating communication technologies
into vehicles. These technologies enable cars to communicate
with each other (V2V - vehicle-to-vehicle), with infrastructure
(V2I - vehicle-to-infrastructure), and with other devices (V2X
- vehicle-to-everything), improving overall transportation
efficiency, safety, and convenience. Beyond security, ML
algorithms can predict potential vehicle faults before they occur
by analyzing historical data and identifying patterns that
typically precede equipment failures. This predictive
maintenance approach reduces the risk of malfunctions that
could be exploited by cyber threats. However, while ML
significantly enhances automotive security, it also presents
challenges, such as ensuring the privacy of collected data,
protecting against manipulation of ML models, and
maintaining regular updates to keep pace with evolving cyber
threats [44].
6.5 Healthcare
IoT devices, such as wearable health monitors, connected
medical equipment, and patient tracking systems, are
increasingly integral in the healthcare sector. These devices
collect, transmit, and process vast amounts of sensitive patient
data, making robust security measures essential. ML
algorithms can enhance the security of data transmission
between IoT devices and central servers by ensuring encryption
standards and identifying potential intercepts or data leaks in
real time. Additionally, ML can automate responses to security
threats, such as temporarily restricting access or alerting
security personnel upon detecting suspicious activity, thereby
reducing the need for manual monitoring.
6.6 Supply Chain Monitoring
In supply chain management, ML models monitor the integrity
of goods, particularly in sensitive industries, by detecting
tampering or deviations in environmental conditions. This
enhances supply chain security and reliability. ML models
analyze data from various sources, including sensors and IoT
devices attached to products or packaging, to ensure that goods

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remain in their intended state throughout the supply chain. This
could involve monitoring for signs of tampering, damage, or
unauthorized access to the products [45, 46].
7. IoT SECURITY SOLUTIONS BASED
ON DL
Deep learning often employs multi-layer perceptrons (MLPs)
and feed-forward artificial networks (FFANs). The traditional
MLP architecture consists of three layers: the input layer, one
or more hidden layers, and the output layer. In an AI network,
each node in a layer is connected to a specific value in the
preceding layer, ultimately linking back to the input layer.
During model training, MLP utilizes backpropagation to adjust
the internal weight values [47]. This MLP network is applied
to analyze the NSL-KDD dataset for malware detection,
explain IoT parameters, identify malicious traffic originating
from IoT devices, and develop a model for intrusion detection
[48].
Recurrent neural networks (RNNs) are another model type that
uses neural feed-forward networks but includes an internal state
or memory to manage sequential data effectively. This
characteristic makes RNNs particularly useful for IoT security,
natural language processing, and speech recognition [49]. IoT
devices generate a significant amount of sequential data, such
as time-varying information and network traffic flows. The
recurrent connections in neural networks allow them to detect
potential security vulnerabilities when a threat's
communication patterns evolve over time. The RNN's ability to
predict time series, supported by its long short-term memory
(LSTM), enables it to recall previous inputs. For instance,
LSTM-based recurrent networks can be used to identify and
classify malicious applications and detect intrusions, along
with other security-related tasks [50].
Various deep learning models and hybrid network models can
be employed to detect and prevent malware, spoofing, and
computer virus attacks across a wide range of IoT devices [51].
One such model is a deep belief network (DBN)-based security
model, which can be used to safeguard IoT devices [52].
Researchers have explored multiple deep learning approaches,
categorizing them as discriminative when human intervention
is required and generative when none is needed. Hybrid
systems may also be utilized when the data quality necessitates
it [53-62].
8. CONCLUSION
This paper gives a review of the literature on IoT security
awareness, focusing on the IoT model, IoT-based intelligent
environments, and the security challenges addressed by
machine learning solutions. We conducted an evaluation of the
knowledge base surrounding IoT security intelligence,
examining the IoT paradigm, smart environments built on IoT,
associated security issues, and the machine learning solutions
that can mitigate these problems. Securing IoT devices and
systems requires a comprehensive analysis of IoT system
architectures and the various cyberattacks that can compromise
them layer by layer. In this study, we explored how different
machine learning and deep learning technologies can be
leveraged to enhance IoT security. For IoT security to be truly
effective, it must be underpinned by machine learning or deep
learning models that are driven by relevant data attributes.
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