METHODS OF INFORMATION SECURITY IN THE INTERNET OF THINGS (IOT) NETWORKS USING QUANTUM MACHINE LEARNING - Research paper

Aug 3, 2026

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The development of the Internet of Things (IoT) introduces serious security risks driven by vulnerable devices and heterogeneous network connections. Limited computing resources make it difficult to apply conventional protection such as encryption and intrusion detection systems, while IoT dynamism and high data complexity amplify the challenge. Traditional machine learning approaches face scalability constraints, weak handling of new threat types, and slow anomaly response, increasing cyberattack exposure. This paper presents a quantum machine learning (QML) approach for detecting threats and anomalies in IoT networks using QML models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

The security challenges of IoT networks

Limitations of traditional security and machine learning

Motivation for quantum machine learning

Literature review

Classical data protection and its constraints

QML for threat recognition and anomaly detection

122 Scientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 22, JUNE 2025 DOI: 10.37943/22JIEN1491 Assemgul Sadvakassova Master of Engineering Science, Senior-Lecturer at the Department of Intelligent Systems and Cybersecurity [a.sadvakassova@astanait.edu.kz](a.sadvakassova@astanait.edu.kz), [orcid.org/0000-0003-0164-6121](orcid.org/0000-0003-0164-6121)[ ](orcid.org/0000-0003-0164-6121)Astana IT University, Kazakhstan Alimzhan Yessenov PhD candidate, Senior-Lecturer at the Department of Intelligent Systems and Cybersecurity [a.yessenov@astanait.edu.kz](a.yessenov@astanait.edu.kz), [orcid.org/0009-0006-8997-3926](orcid.org/0009-0006-8997-3926)[ ](orcid.org/0009-0006-8997-3926)Astana IT University, Kazakhstan METHODS OF INFORMATION SECURITY IN THE INTERNET OF THINGS (IOT) NETWORKS USING QUANTUM MACHINE LEARNING Abstract: The development of the Internet of Things (IoT) poses serious security challenges due to the vulnerability of devices and network connections. IoT devices often have limited computing resources, which makes it difficult to implement traditional security methods such as encryption and intrusion detection systems. In addition, the dynamic nature and high complexity of IoT networks create additional security challenges, requiring the development of new, more effective security methods. Traditional machine learning algorithms used to protect IoT networks have their limitations in terms of scalability and ability to effectively cope with large volumes of data, as well as new types of threats. These algorithms are often unable to quickly respond to anomalies, which significantly increases the risk of cyberattacks. In this regard, there is a need to find new solutions to improve the security of IoT networks. This paper proposes a new approach to IoT security using quantum machine learning (QML), which combines the capabilities of quantum computing with machine learning algorithms to create more powerful models for detecting threats and anomalies in IoT networks. We analyze various QML algorithms, such as quantum support vector machines (QSVMs), quantum neural networks (QNNs), and quantum reinforcement learning (QRL), applied to solve security problems. Experiments conducted using the dataset confirm the effectiveness of quantum algorithms compared to traditional machine learning methods. The results show that QML models provide higher accuracy in detecting threats and anomalies, and significantly reduce the time spent on processing and training compared to classical methods. In conclusion, we argue that using QML to protect IoT networks can significantly improve their security and efficiency, opening up new prospects for further research in this area. Keywords: Internet of Things (IoT), information security, quantum machine learning (QML), machine learning algorithms, IoT network security, quantum support vector machines (QSVM), quantum neural networks (QNN), quantum reinforcement learning (QRL), data security. Introduction The Internet of Things (IoT) is a dynamically growing ecosystem in which devices interact with each other and with external services to exchange data and perform various functions. According to analytical studies, more than 50 billion IoT devices are expected to be connected by 2030, which in turn leads to a significant increase in the volume of information transmit Copyright © 2025, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 19.03.2025 Accepted: 03.06.2025 Published: 30.06.2025 DOI: 10. 37943/22JIEN1491 © Assemgul Sadvakassova, Alimzhan Yessenov 123 ted. However, the growth of this technology is accompanied by an increase in the number of cyber-attacks, making data security one of the key challenges in the IoT sector [1]. Traditional security methods, such as intrusion detection systems (IDS), often fail to provide the required effectiveness due to the limited computing re

FAQs

Why are traditional security methods less effective in IoT networks?

IoT devices have limited computing resources, and network architectures are complex. This reduces the effectiveness of intrusion detection systems and makes it harder for traditional algorithms to process large data volumes and respond quickly to new threats.

What is the proposed solution using quantum machine learning?

The paper proposes using quantum machine learning (QML) to detect threats and anomalies in IoT networks by combining quantum computing capabilities with machine learning models.

How do QML models compare with classical machine learning in the experiments?

Experiments using a dataset show that quantum algorithms achieve higher accuracy for threat and anomaly detection, while reducing processing and training time compared with classical methods.

Aug 3, 2026

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