Machine Learning Based Attack Detection for Quantum Key Distribution
Research output: Contribution to journal › Conference article › Contributed › peer-review
Contributors
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 language | English |
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Pages (from-to) | 1-6 |
Journal | IEEE World Forum on Internet of Things (WF-IoT) |
Publication status | Published - 2023 |
Peer-reviewed | Yes |
Conference
Title | 9th IEEE World Forum on Internet of Things |
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Abbreviated title | WF-IoT 2023 |
Conference number | 9 |
Duration | 12 - 27 October 2023 |
Website | |
Location | Aveiro Congress Center & online |
City | Aveiro |
Country | Portugal |
External IDs
ORCID | /0000-0001-8469-9573/work/162348284 |
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