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Title: Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats
Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.  more » « less
Award ID(s):
1905043
PAR ID:
10533340
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Machine Learning and Knowledge Extraction
Volume:
6
Issue:
2
ISSN:
2504-4990
Page Range / eLocation ID:
944 to 964
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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