Machine Learning Based Attack Detection for Quantum Key Distribution

Research output: Contribution to journalConference articleContributedpeer-review

Abstract

In quantum communication era, where quantum computing capabilities can be used in cyber attacks, it is important that Internet of Things (IoT) devices, which have limited energy and computing power compared to central devices, can communicate securely with control devices. In our study, we performed a simulation based on two scenarios for quantum key distribution (QKD) with the absence and presence of attack between server and IoT device. Security of communication for IoT device is achieved by detecting the attacks on QKD process with a machine learning (ML) algorithm. Unlike traditional cyber security methods, the ML algorithm helped to detect attacks with 100% probability without interrupting the flow of information between the server and the IoT device. The proposed simulation can also be generalised for being suitable for other applications where quantum communications are used.

Details

Original languageEnglish
Pages (from-to)1-6
JournalIEEE World Forum on Internet of Things (WF-IoT)
Publication statusPublished - 2023
Peer-reviewedYes

Conference

Title9th IEEE World Forum on Internet of Things
Abbreviated titleWF-IoT 2023
Conference number9
Duration12 - 27 October 2023
Website
LocationAveiro Congress Center & online
CityAveiro
CountryPortugal

External IDs

ORCID /0000-0001-8469-9573/work/162348284