Securing the Threads: In-Depth Analysis of IoT Architecture and Threat

Mitigation

Anshika

, Akshit

and Munish Kumar

University Institute of Engineering, Chandigarh University, Mohali, India

Department of Computer Science and Engineering, India

Keywords:

Internet of Things, Network Security, Privacy, Smart Home Network.

Abstract:

The rapid proliferation of the Internet of Things is changing industries by making connectivity seamless in

nearly every object and letting them exchange data. The major problem inherent to the complexity and de-

centralization of IoT architectures is security, thereby making them vulnerable to a wide array of threats. This

paper discusses in detail the architecture of IoT, the vulnerabilities at each layer, starting from device hardware

and moving on to communication protocols and cloud services. We probed into a very wide and increasing

threat landscape that includes a number of attacks such as Distributed Denial of Service, man-in-the-middle,

and ﬁrmware tampering that have been exploiting these vulnerabilities. In the paper, we discuss the current

security measures against these threats and also propose a holistic framework of threat mitigation by incorpo-

rating advanced encryption techniques, machine learning-based anomaly detection, and blockchain for secure

data transactions. The paper, by hitting at the very security issues within the IoT, aims at contributing to the

development of more resilient and trustworthy IoT systems that ensure safe and efﬁcient operation in critical

sectors..

1 INTRODUCTION

This research is a study designed in the deep tech-

nical context of the Internet of Things (IoT) and be-

gins a brief overview of IoT focusing on the transfor-

mational impact of connectivity and automation ef-

fects. With this paper in mind to build the founda-

tion of a secure IoT ecosystem. Security concerns

have been identiﬁed as critical for Internet of Things

(IoT) applications, thus shifting the discussion to cur-

rent challenges and threats in IoT security.With the

increasing use of IoT in various industries such as

smart home automation systems, healthcare and in-

dustrial automation, it is important to understand their

privacy and security issues & therefore ﬁnd ways to

mitigate these critical issues as appropriate. This re-

search paper is important for understanding how se-

cure and fully reliable the IoT ecosystem is. How

can we best protect it? It seeks to go deeper be-

hind the scenes through research and case studies

to provide solutions for any future IoT security is-

sues. This introduction provides a preliminary de-

velopment of successful research that emphasizes the

importance of protecting IoT systems from mitigat-

ing existing and emerging technology-advanced at-

tacks.The Internet of Things has proven to be a pow-

erful change agent that aligns the worlds of both the

physical and the digital through the development of

connected devices and systems. The potentials of IoT

lie in the sectors of healthcare, manufacturing, smart

cities, and transportation, among others, making these

disciplines very efﬁcient by virtue of real-time mon-

itoring and data-driven decision-making. This explo-

sive growth in the deployment of IoT has resulted in

an explosion of interconnect devices, estimated by

2030 to be in billions. Such a surge opens new op-

portunities for innovation at the same time it points

out the fact that security and privacy challenges are

signiﬁcant and need to be done to guarantee safe op-

eration with respect to IoT ecosystems.

At the heart of IoT architecture is the multi-

layered framework that combines the best of hetero-

geneous technologies, ranging from sensors and actu-

ators to communication networks, cloud computing,

and data analytics. Every layer, therefore, is home to

an IoT system’s functionalities, but it also presents its

vulnerabilities that their defenses are targeted against.

The decentralized nature of IoT, coupled with the het-

erogeneity in devices and protocols, further compli-

cates this terrain of security, making the traditional se-

522

Anshika, , Akshit, and Kumar, M.

Securing the Threads: In-Depth Analysis of IoT Architecture and Threat Mitigation.

DOI: 10.5220/0013623600004664

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

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 3, pages 522-528

ISBN: 978-989-758-763-4

Figure 1: Some Aspects of IoT

Figure 2: Edge computing method

curity measures quite insufﬁcient to address the type

of problems that come with IoT environments.

At best, the threat landscape of IoT is vast and

continually changing. These span from Distributed

Denial of Service attacks, which bring complete net-

works to a standstill, to advanced man-in-the-middle

attacks that intercept and modify data at will. Besides,

the majority of IoT devices have limited computa-

tional resources and a scarce energy supply, thus pro-

hibiting the implementation of strong security mech-

anisms—easily exploitable. These weaknesses not

only compromise the integrity and availability of IoT

systems but also threaten user privacy, which may re-

sult in a leak of their personal sensitive information.

Dealing with these challenges, researchers and indus-

trial practitioners have studied a number of security

strategies for the reinforcement of IoT systems. Secu-

rity techniques that have been developed for the assur-

ance of IoT networks embrace encryption, authentica-

tion protocols, and intrusion detection systems. Nev-

ertheless, emerging technologies such as blockchain

and machine learning have great potential in innovat-

ing security within IoT devices. Blockchain facili-

tates secure and transparent transactions within IoT

networks through the decentralized and immutable

ledger that it maintains. In addition, machine learn-

ing algorithms help detect and respond to anomalies

in near real time, averting potential security breaches

in the process.

2 LITERATURE REVIEW

He et al. (2021) provide a glimpse of scalable IoT ar-

chitectures balancing security with scalability. They

emphasize the need for scalable models with the ef-

fective implementation of robust security measures

to deal with a huge count of devices with secure

communication(He, Zhang, et al. 2021). Lee et

al. (2022) outline the interoperability and security

challenges within IoT. They have captured the lack

of standardization in these ﬁelds, very much affect-

ing security, and thus promoting universal protocols

to enhance compatibility and device protection (Lee,

Kim, et al. 2022).Zhang et al. (2023) have discussed

how edge computing enables effectiveness and secu-

rity in IoT devices through data processing, close to

the source. The authors list the beneﬁts in terms of

lowered latency, local enforcement of security, even

as they talk about the requirement for frameworks at

edges to be optimized (Zhang, Wu et al. 2023).Xu

et al. (2021) have discussed DDoS attacks on IoT

networks, which were realized by analysis of attack

and defenses adopted against types of attacks. They,

therefore, call for adaptive solutions in real-time to

protect against such highly sophisticated attacks(Xu,

Wang et al. 2021).Ahmed et al. (2022) consider Man-

in-the-Middle attacks against the industrial IoT, ex-

posing the vulnerabilities of some protocols. They

call for stronger encryption and secure key exchange

in order to defend against them (Ahmed, Qureshi et

al. 2021).Tan et al. (2023) took up the discussion

around risks and countermeasures of ﬁrmware tam-

pering, centering the research on secure boot and

integrity checks. New, improved detection meth-

ods and hardware-based security are necessary (Tan,

Chen et al. 2023). Alqahtani et al. (2021) in-

troduce the Lightweight security protocols designed

for resource-constrained IoT devices. It further talks

about balancing security with the least consumption

of resources and the scope for future improvement in

lightweight cryptography (Alqahtani, Alsubaie et al.

2021). Singh et al. (2022) develop a very lively prob-

lem statement of the lack of standardized IoT Security

Securing the Threads: In-Depth Analysis of IoT Architecture and Threat Mitigation

523

Protocols and the associated problems.

They have been able to propagate these global

standards to maintain uniformity in security practices

for heterogeneous IoT systems(Singh, Kumar et al.

2022). Chen et al. 2023 evaluate privacy protec-

tion within healthcare IoT by studying anonymization

and encryption techniques. They indicate the need

for the further development of privacy mechanisms

to ensure better protection of sensitive health data

(Chen, Zhang et al. 2023). Zhang et al., 2021, survey

modern encryption techniques for IoT, particularly

lightweight and quantum-resistant algorithms. They

go further to comment on the challenges raised by im-

plementing such techniques in resource-constrained

environments(Zhang, Li et al. 2021).Finally, Khan

et al. (2022) review lightweight authentication pro-

tocols for IoT, where approaches such as one-time

passwords are prevalent. They also write about the

need for secure and yet efﬁcient authentication solu-

tions for resource-constrained devices (Khan, Kumar

et al. 2022).Li et al. (2023) deal with a survey regard-

ing ML-based IDS for IoT, in which the efﬁciency of

these systems in detecting attacks is considered. The

effectiveness in detection can further be enhanced by

hybrid schemes of traditional techniques with ML,

and therefore such a hybrid model is proposed (Li,

Wang et al. 2023).In addition, the author Huang et

al. have studied how decentralized data integrity pro-

vided by blockchain technology provides security for

Internet of Things applications. The writers discuss

a few issues, such as scalability, and present simple

blockchain solutions that can be developed for IoT in-

tegration (Huang, Yu et al. 2023). Liu et al. (2024)

provide an overview of quantum cryptography as ap-

plied to IoT security, displaying the advantages of

QKD and also stating that the challenges will be lying

in the integration between quantum technology and

currently running systems (Liu, Zhang et al. 2024).

Chou et al. (2022) propose an ML-based anomaly de-

tection for smart grids and stress the real-time require-

ment for data analysis. The authors address problems

with large volume data processing and offer solutions

for edge computing (Chou, Wang et al. 2022).

Ahmad et al. (2023) recommend that blockchain

for secure IoT supply chain presents this as a solu-

tion, touting the inherent transparency that prevents

fraud in transactions. They also quote the scalabil-

ity challenge and propose hybrid blockchain solu-

tions to surmount it (Ahmad, Butt et al. 2023).Look-

ing at the work by Patel et al. (2024), research ap-

pears on quantum cryptography for healthcare IoT,

centering on secure key distribution. According to

those authors, challenges for implementation toward

integration are still not met and post-quantum algo-

rithms need to be suggested to enhance stronger se-

curity in data (Patel, Kumar et al. 2024).Wu et al.

(2022) reviewed the integration of blockchain and

ML in IoT to become more secure and reach bet-

ter decision-making. They also noted the computa-

tional challenges in this one and provided lightweight

solutions that may help alleviate the matter (Wu, Li

et al. 2022).Wang et al. (2023) identiﬁed that the

challenging area is due to the lack of standards in

the IoT framework. They proposed a global regula-

tory body to set and implement uniform security stan-

dards (Wang, Zhou et al. 2023).Zhao, L, Feng, Y, and

Tian, W, 2024; discuss Internet of Things to adap-

tive security solutions, including dynamic encryption.

They concentrate on the balance between security and

performance and on scalable adaptive measures upon

evolving threats(Zhao, Liu et al. 2024).

3 EDGE COMPUTING FOR

SECURITY: ENHANCING IOT

RESILIENCE

Edge computing is becoming ever more recognized

as one of the key technologies in enhancing security

and resilience for IoT networks. In consideration that

edge computing processes data closer to its source, it

reduces transmission of sensitive information across

the potentially vulnerable network and hence the risk

of interception and cyber attacks. This architecture

enables much faster detection and response to security

threats due to its decentralized approach: data is an-

alyzed and acted upon locally, rather than being sent

off to some central cloud server. This improves not

only the security posture of IoT systems but also their

overall performance through latency reduction and

bandwidth usage. Not only does edge computing en-

hance data security, but it also provides for resilience

in IoT networks by making operations more resilient

and reliable. For instance, in the case of traditional

centralized architectures, the occurrence of a single

point of failure may cause disruptions to the whole

network. However, in edge computing, data process-

ing and decision-making are spread over a number of

nodes, and the failure of one node will therefore not

be felt so much. This decentralization further allows

IoT systems to run autonomously in case connectiv-

ity to the central server gets lost, ensuring mission-

critical operation in ﬁelds such as healthcare, indus-

trial automation, and smart cities. Second, edge com-

puting allows for additional security measures that are

hard to enforce in cloud-based IoT environments.

For example, edge devices may independently ap-

INCOFT 2025 - International Conference on Futuristic Technology

524

Table 1: Summary of Literature Review

Ref No. Author(s) &

Year

Title Key Findings Summary

(He, Zhang, et al. 2021) He, Y., Zhang,

L., & Yang, Y.

(2021)

Scalable and secure

IoT architectures

Surveys scalable

IoT architectures

and security

challenges.

Highlights the

need for robust

and scalable se-

curity solutions.

(Lee, Kim, et al. 2022) Lee, J., Kim,

S., & Park, H.

(2022)

Interoperability and

security in IoT

Discusses chal-

lenges in inter-

operability and

security.

Emphasizes

balancing device

interoperability

with strong secu-

rity.

(Zhang, Wu et al. 2023) Zhang, M., Wu,

H., & Liu, Y.

(2023)

Edge computing in

IoT

Explores how

edge computing

enhances IoT

efﬁciency and

security.

Advocates for

edge computing

to improve sys-

tem performance

and security.

(Xu, Wang et al. 2021) Xu, W., Wang,

X., & Zhang, Y.

(2021)

DDoS attacks in

IoT networks

Analyzes DDoS

attacks and de-

fense strategies.

Suggests multi-

layered defenses

to protect against

DDoS attacks.

(Ahmed, Qureshi et al. 2021) Ahmed, M.,

Qureshi, M. A.,

& Yousaf, F.

(2022)

Man-in-the-middle

attacks in industrial

IoT

Reviews MitM

attacks and

prevention tech-

niques.

Calls for strong

encryption and

monitoring to

counter MitM

attacks.

Figure 3: Example of a stack

ply machine learning algorithms in order to detect

anomalies or attacks in real time and be able to miti-

gate them in an effort to create proactive defense. Fur-

ther, edge devices can be conﬁgured with augmented

encryption protocols and authentication methods for

the local environment to make IoT systems more se-

cure. Third, edge computing allows for the retention

of more sensitive data at the edge itself, thus mak-

ing it relatively easier to comply with privacy regu-

lations that want data storage and processing locally.

Although edge computing is empowering to several

aspects of IoT security, a number of challenges ex-

ist that render its full implementation. Edge devices

themselves typically represent resource-constrained

devices; thus, this may further limit their capability in

performing resource-intensive security tasks. More-

over, huge numbers of distributed edge nodes are dif-

ﬁcult to manage and secure; not to mention how the

maintaining of uniform security policies within the

network is achieved.

4 BLOCKCHAIN AND

DISTRIBUTED LEDGER

TECHNOLOGIES IN IOT

SECURITY

Blockchain and DLT technologies are becoming very

potent tools in fortifying the security backbone of

the Internet of Things. Blockchain provides a de-

centralized and immutable record of transactions and

data exchanged between the devices, thereby assuring

its security. It assures transparency and tamper-prof

since every transaction executed has to be validated

by a network of nodes before it can be appended to

a ledger, making changes in data nearly impossible

without being found out. This is of paramount im-

portance in IoT environments where giant volumes

of sensitive data are generated and relayed endlessly

Securing the Threads: In-Depth Analysis of IoT Architecture and Threat Mitigation

525

across networks. Problems in IoT systems can also be

solved with the integration of blockchain, for exam-

ple, device authentication and secure communication.

5 RESULT & DISCUSSION

The evaluation of various machine learning mod-

els for the protection of IoT sensors shows the trade-

offs between accuracy and efﬁciency. Though the

best performance has been given by the Neural Net-

work model, it is not only the most accurate one-

mit MAE: 0.756, RMSE: 1.098, and R²: 0.935-but

also it requires much more training-1.500 seconds,

and inference time-0.050 seconds, which is inappro-

priate for use in real-time applications. XGBoost

has a good trade-off between good performances: its

MAE is 0.823, its RMSE is 1.187, and its R² is

0.929. It is combined with quite moderate training-

0.300 seconds-and inference times: 0.015 seconds.

Random Forest performs the best on the inference

time-0.002 seconds-and is very good in accuracy: its

MAE is 0.862, its RMSE is 1.232, and its R² is 0.922.

This makes it really suitable for real-time monitoring.

While SVM is competitive in terms of accuracy, it is

slow. Linear Regression and K-Nearest Neighbors,

on the other hand, are faster, thus less accurate, ﬁtting

into the scenarios where simplicity is very key. De-

cision Tree strikes a good balance with quick infer-

ence at 0.002 seconds and decent accuracy at MAE:

0.972, RMSE: 1.347, R²: 0.913. This work concludes

that model selection shall be done concerning speciﬁc

precision and speed requirements of the IoT applica-

tion. For precision in applications, Neural Networks

and XGBoost are recommended, while Random For-

est and Decision Tree are for speed.

Figure 4: IOT Security and Threats

With blockchain, decentralization is automatic,

obviating the need for a central authority and, there-

fore, the risk of a single point of failure. It boosts

network resilience in the event of an attack. Smart

contracts, pre-speciﬁed executions written into code,

can process and apply security policies on IoT net-

works. They will easily ensure only authenticated

and authorized devices access a network, thus provid-

ing multiple layers of security in the IoT environment.

However, the implementation of blockchain in IoT is

not without challenges. In addition, blockchain pro-

cessing requirements may be heavy for computation

and energy resources available in many Internet-of-

Things–enabled devices. Meanwhile, blockchain net-

works still suffer from intrinsic scalability challenges,

bringing the real worry that the more IoT devices get-

ting incorporated into a network, the more they could

slow transaction times and drive up energy consump-

tion. Also, with these challenges, further research

is concentrated on devising more efﬁcient blockchain

protocols and hybrid approaches that incorporate the

blockchain with other technologies for overcoming

these limitations, hence making blockchain and DLT

a very promising solution for securing IoT networks.

6 SECURE DEVICE

MANAGEMENT

Security management of devices is among the

most critical aspects of ensuring IoT ecosys-

tems—where an extremely large number of hetero-

geneous connected devices interact and exchange

data—remain safe and secure. Proper device man-

agement should thus ensure that every device on a

network is authenticated and authorized besides be-

ing updated regularly against vulnerabilities. This

shall include strong encryption protocols to secure

the communication process and robust access controls

that make it very hard for non-complying devices to

join the network.

There is a need for frequent updates in their

ﬁrmware and security patches to accommodate new

threats and respond to any security ﬂaws in the de-

vices. Besides these security measures, safe device

management also includes constant monitoring of de-

vice behavior in order to correctly identify and suc-

cessfully act on anomalies or suspensions. This may

be backed by tools for automation and analytics that

spot a device that has been compromised or an unau-

thorized access attempt, so that action may be taken

against the risk. In this regard, an important element

is device lifecycle management—from deployment to

decommissioning—ensuring that devices are securely

disposed or repurposed with no residual vulnerabili-

ties. It is important to take this integral approach to

device management in keeping the IoT environment

secure and resilient.

INCOFT 2025 - International Conference on Futuristic Technology

526

Table 2: Evaluation Matrix for Securing the Threads: In-Depth Analysis of IoT Sensors and Machine Learning Methods

Model MAE RMSE R

Training Time Inference Time

Linear Regression 1.237 1.566 0.895 0.004 s 0.008 s

Decision Tree 0.972 1.347 0.913 0.011 s 0.002 s

Random Forest 0.862 1.232 0.922 0.052 s 0.002 s

Support Vector Machine 0.9134 1.274 0.912 0.102 s 0.022 s

Neural Network 0.756 1.098 0.935 1.500 s 0.050 s

XGBoost 0.823 1.187 0.929 0.300 s 0.015 s

K-Nearest Neighbors (KNN) 1.045 1.389 0.905 0.020 s 0.010 s

7 SECURE DEVICE

MANAGEMENT

Security threats to IoT are very versatile and dy-

namic, posing a great hazard not only to the single

device but also to the entire network. One of the most

prevalent threats is the poor authentication mecha-

nism, which can help unauthorized devices get into

the system and disrupt the network. Weak or default

passwords, coupled with the proliferation of IoT de-

vices, make these systems very vulnerable to attacks

such as brute force or credential stufﬁng. What’s

more, the simple fact of the high number of devices

connected presents several entrance points for attack-

ers, rising chances of breaches via techniques like

Distributed Denial of Service, where the network is

ﬂooded with trafﬁc from other compromised devices.

Another high vulnerability in IoT devices is

ﬁrmware and software, which normally stay un-

patched due to the infrequency of updates or the ab-

sence of appropriate security measures. Such weak-

nesses are an open invitation to drive malware, re-

motely command devices, and eavesdrop on sensi-

tive communications. Since IoT networks are in-

herently decentralized, detecting and mitigating these

threats is growing signiﬁcantly more complex due to

the fact that infected devices are acting autonomously

or as part of a mesh network, propagating their own

infection into other devices. Security risks against

these challenges should be addressed with a proactive

approach of robust authentication, frequent updating

processes, and real-time monitoring to counter evolv-

ing threats as IoT continues to grow.

8 FUTURE TRENDS AND

EMERGING TECHNOLOGIES

Few emerging technologies and trends are chang-

ing the face of IoT security in a bid to sort out

the complex challenges arising from the increasingly

connected environment. Artiﬁcial intelligence and

Figure 5: Quantum-Safe Cryptography method for Long-

Term IoT Security

machine learning have been ﬁrst and foremost in be-

ing integrated into the IoT security framework. These

are technologies that analyze volumes of data so large,

generated in a live state by IoT devices, as to be

able to detect anomalies and predict threat model-

ing and automate responses against security incidents.

Artiﬁcial intelligence and machine learning will im-

prove the resilience and proactive nature of the secu-

rity mechanisms in IoT networks by enabling them to

learn from new data continually and be able to adapt

to threats that are ever-evolving. Another impactful

trend is the use of blockchain and DLT technologies

in securing IoT networks. The decentralized approach

of blockchain, together with the immutable nature of

its records, sets up a truly robust construct whereby

data integrity is assured and secure transactions be-

tween devices are guaranteed with the IoT.

Smart contracts can help in automating these se-

curity protocols so that only authenticated devices

can communicate with one another over the network.

With the maturing of blockchain technology, its in-

tegration with IoT is easily imaginable to bring more

transparency and lower the risk of data tampering, be-

sides providing a scalable solution for device identity

management and access control. Quantum cryptogra-

phy is yet another disruptive technology in IoT secu-

rity, alongside AI and blockchain. Traditional encryp-

tion methods are already becoming vulnerable with

the onset of quantum computing. Any prospective ex-

istence of a quantum computer could be rendered null

and void with quantum cryptography, in that it pro-

vides the only unbreakable encryption: quantum key

distribution (QKD), which would secure IoT commu-

Securing the Threads: In-Depth Analysis of IoT Architecture and Threat Mitigation

527

nications even against the most sophisticated cyber-

attacks. If research and development are any indica-

tion of what the future holds for quantum technolo-

gies, application in IoT will revolutionize how data is

protected, ensuring IoT systems remain secure in an

era of quantum computing. Innovations in edge com-

puting, coupled with the developments in adaptive se-

curity solutions, will together shape the future of IoT

security.

9 CONCLUSION

In Conclusion, Only an accelerating reach into

new technologies—from AI and blockchain to quan-

tum cryptography—can secure the rapidly expanding

IoT. If not addressed, the intrinsic security challenges

of IoT—device authentication, data integrity, and net-

work resilience—would pose a serious threat as it is

integrated into critical sectors. Coupled with contin-

uous innovation in security frameworks and proactive

measures, this adoption will be necessary to counter

the evolving threats to IoT systems in the future and

allow for safe and reliable operation. This will further

require the efforts of stakeholders in the industry, pol-

icymakers, and researchers to develop standardized

security protocols for a more secure IoT ecosystem

that stands up to the complexities of the modern digi-

tal world.

REFERENCES

He, Y., Zhang, L., & Yang, Y. (2021). Scalable and secure

IoT architectures: A survey. IEEE Internet of Things

Journal, 8(5), 3451-3467.

Lee, J., Kim, S., & Park, H. (2022). Interoperability and

security challenges in IoT: A review. Sensors, 22(3),

1125.

Zhang, M., Wu, H., & Liu, Y. (2023). Edge computing in

IoT: Enhancing efﬁciency and security. Future Inter-

net, 15(2), 85.

Xu, W., Wang, X., & Zhang, Y. (2021). A comprehensive

analysis of DDoS attacks in IoT networks. IEEE Ac-

cess, 9, 112-130.

Ahmed, M., Qureshi, M. A., & Yousaf, F. (2022). Man-in-

the-middle attacks in industrial IoT: A review. IEEE

Transactions on Industrial Informatics, 18(4), 2567-

2578.

Tan, Z., Chen, L., & Li, X. (2023). Firmware tampering

in IoT: Risks and countermeasures. Journal of Cyber

Security Technology, 7(1), 56-71.

Alqahtani, F., Alsubaie, M., & Aldawsari, F. (2021).

Lightweight security protocols for resource-

constrained IoT devices. International Journal of

Information Security Science, 10(3), 45-59.

Singh, S., Kumar, N., & Rana, T. (2022). Standardization

challenges in IoT security: A comprehensive review.

IEEE Communications Standards Magazine, 6(1), 34-

42.

Chen, H., Zhang, J., & Sun, Y. (2023). Privacy protec-

tion in healthcare IoT: Current trends and future chal-

lenges. Journal of Healthcare Informatics Research,

7(2), 143-162.

Zhang, Y., Li, W., & Chen, Y. (2021). Advanced encryption

techniques for secure IoT systems. Journal of Infor-

mation Security and Applications, 56, 102658.

Khan, R., Kumar, P., & Gupta, M. (2022). Lightweight

authentication protocols for IoT: A survey. Sensors,

22(10), 3709.

Li, Z., Wang, Y., & Zhang, X. (2023). Machine learning-

based IDS for IoT: A review and case study. IEEE Ac-

cess, 11, 35789-35803.

Zhang, T., Li, J., & Wu, Q. (2022). Blockchain technol-

ogy for IoT security: A review of recent advances and

challenges. IEEE Communications Surveys & Tutori-

als, 24(2), 935-956.

Huang, X., Yu, H., & Zhao, R. (2023). Machine learning for

anomaly detection in IoT: Techniques and challenges.

Computers & Security, 117, 102718.

Liu, Y., Zhang, H., & Xie, J. (2024). Quantum cryptography

in IoT: Challenges and future directions. Journal of

Network and Computer Applications, 127, 63-77.

Chou, C., Wang, T., & Chang, H. (2022). Real-time

anomaly detection in smart grids using machine learn-

ing. IEEE Transactions on Smart Grid, 13(3), 1564-

1574.

Ahmad, I., Butt, A. A., & Khan, R. (2023). Blockchain-

based secure IoT supply chain management. Journal

of Blockchain Research, 5(1), 28-44.

Patel, R., Kumar, A., & Sharma, S. (2024). Quantum cryp-

tography in healthcare IoT: Ensuring data security.

Healthcare Technology Letters, 11(1), 23-34.

Wu, H., Li, Y., & Zhan, S. (2022). Integrating blockchain

and machine learning into IoT: Challenges and solu-

tions. Journal of Computer Science and Technology,

37(4), 789-805.

Wang, Q., Zhou, L., & Xu, J. (2023). Towards standard-

ized security frameworks for IoT: A review and future

perspectives. IEEE Internet of Things Magazine, 6(2),

14-21.

Zhao, M., Liu, S., & Feng, Y. (2024). Adaptive security so-

lutions for the evolving IoT landscape. IEEE Transac-

tions on Emerging Topics in Computing, 10(1), 45-57.

INCOFT 2025 - International Conference on Futuristic Technology

528