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.