Mach. Learn. Knowl. Extr., Volume 6, Issue 2 (June 2024) – 32 articles
Cover Story (view full-size image):
This study presents a “quanvolutional autoencoder” to enhance our understanding of cybersecurity data related to DDoS attacks. It uses randomized quantum circuits to improve how we analyze attack data, offering a solid alternative to traditional neural networks. The model effectively learns representations from DDoS data, with faster learning and better stability than classical methods. These findings suggest that quantum machine learning can advance data analysis and visualization in cybersecurity, highlighting the need for further research in this rapidly growing field. View this paper
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