A case study for cyber-attack detection using quantum variational circuits

被引：0
作者：
Maximilian Moll [1 ]
Leonhard Kunczik [2 ]
机构：
[1] Institut für Theoretische Informatik, Mathematik und Operations Research, Universitat der Bundeswehr Munchen, Werner-Heisenberg-Weg 39, Bavaria, Neubiberg
[2] Research Institute Cyber Defence (CODE), Universitat der Bundeswehr Munchen, Werner-Heisenberg-Weg 39, Bavaria, Neubiberg
来源：
Quantum Machine Intelligence | 2025年 / 7卷 / 1期
关键词：
Cyber-attack detection; Gate-based quantum computers; Quantum machine learning; Quantum variational circuits;
D O I：
10.1007/s42484-025-00277-1
中图分类号：
学科分类号：
摘要：
Identification of malicious attacks in network traffic is one of many important big data problems that have been approached from different directions, including machine learning. In this paper, it is used as an example for investigating the applicability of quantum machine learning to such problems. Particular focus is kept on the NISQ era, in which available computer sizes are typically fairly small. Instead of applying typical cutting and knitting techniques with their associated overhead cost, we use the trainable output layer inherent in many hybrid QML approaches to re-combine the results from a collection of smaller QVCs executed on different machines. It is compared to classical and established quantum approaches on four real-world datasets. © The Author(s) 2025.
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