Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers
The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10415536/ |
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| author | Alon Kukliansky Marko Orescanin Chad Bollmann Theodore Huffmire |
| author_facet | Alon Kukliansky Marko Orescanin Chad Bollmann Theodore Huffmire |
| author_sort | Alon Kukliansky |
| collection | DOAJ |
| description | The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum era machines. In this article, we explore quantum neural networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multilayered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. In addition, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system. |
| format | Article |
| id | doaj-art-0e6826d858b745c0a451d3210f92a5a8 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-0e6826d858b745c0a451d3210f92a5a82025-01-25T00:03:27ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511110.1109/TQE.2024.335957410415536Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum ComputersAlon Kukliansky0https://orcid.org/0009-0003-6743-9018Marko Orescanin1https://orcid.org/0000-0003-3305-8412Chad Bollmann2https://orcid.org/0000-0001-8812-9391Theodore Huffmire3https://orcid.org/0009-0009-8019-7491Naval Postgraduate School, Monterey, CA, USANaval Postgraduate School, Monterey, CA, USANaval Postgraduate School, Monterey, CA, USANaval Postgraduate School, Monterey, CA, USAThe escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum era machines. In this article, we explore quantum neural networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multilayered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. In addition, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.https://ieeexplore.ieee.org/document/10415536/Intrusion detectionnetwork intrusion detection system (NIDS)quantum neural network (QNN) |
| spellingShingle | Alon Kukliansky Marko Orescanin Chad Bollmann Theodore Huffmire Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers IEEE Transactions on Quantum Engineering Intrusion detection network intrusion detection system (NIDS) quantum neural network (QNN) |
| title | Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers |
| title_full | Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers |
| title_fullStr | Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers |
| title_full_unstemmed | Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers |
| title_short | Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers |
| title_sort | network anomaly detection using quantum neural networks on noisy quantum computers |
| topic | Intrusion detection network intrusion detection system (NIDS) quantum neural network (QNN) |
| url | https://ieeexplore.ieee.org/document/10415536/ |
| work_keys_str_mv | AT alonkukliansky networkanomalydetectionusingquantumneuralnetworksonnoisyquantumcomputers AT markoorescanin networkanomalydetectionusingquantumneuralnetworksonnoisyquantumcomputers AT chadbollmann networkanomalydetectionusingquantumneuralnetworksonnoisyquantumcomputers AT theodorehuffmire networkanomalydetectionusingquantumneuralnetworksonnoisyquantumcomputers |