Quantum machine learning for enhanced intrusion detection
| dc.contributor.advisor | Meyer, Thomas | |
| dc.contributor.author | Valjee, Ameel | |
| dc.date.accessioned | 2026-06-30T10:16:54Z | |
| dc.date.available | 2026-06-30T10:16:54Z | |
| dc.date.issued | 2026 | |
| dc.date.updated | 2026-06-30T10:14:58Z | |
| dc.description.abstract | A vital component in ensuring network security is an Intrusion Detection System (IDS), and classical Machine Learning (ML) models often struggle to keep pace with sophisticated cyber threats. Quantum Computing, a probabilistic computational class utilising the principles of quantum mechanics, has shown its potential to address some of these challenges through quantum parallelism and entanglement. In this work, we explore the use of Quantum Machine Learning (QML), specifically Quantum Convolutional Neural Network (QCNN) and Quantum Support Vector Machine (QSVM), to determine the benefits of using quantum kernels over classical kernels. We investigate multiple ansatz designs, and entanglement patterns, that generate sufficient representations of vectors in the Hilbert space to distinguish normal from malicious activity. The results have shown improvements in both models compared to their classical counterparts, with QSVM being the best performing when circuit block entanglement is used. | |
| dc.identifier.apacitation | Valjee, A. (2026). <i>Quantum machine learning for enhanced intrusion detection</i>. (). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/43422 | en_ZA |
| dc.identifier.chicagocitation | Valjee, Ameel. <i>"Quantum machine learning for enhanced intrusion detection."</i> ., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2026. http://hdl.handle.net/11427/43422 | en_ZA |
| dc.identifier.citation | Valjee, A. 2026. Quantum machine learning for enhanced intrusion detection. . University of Cape Town ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/43422 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Valjee, Ameel AB - A vital component in ensuring network security is an Intrusion Detection System (IDS), and classical Machine Learning (ML) models often struggle to keep pace with sophisticated cyber threats. Quantum Computing, a probabilistic computational class utilising the principles of quantum mechanics, has shown its potential to address some of these challenges through quantum parallelism and entanglement. In this work, we explore the use of Quantum Machine Learning (QML), specifically Quantum Convolutional Neural Network (QCNN) and Quantum Support Vector Machine (QSVM), to determine the benefits of using quantum kernels over classical kernels. We investigate multiple ansatz designs, and entanglement patterns, that generate sufficient representations of vectors in the Hilbert space to distinguish normal from malicious activity. The results have shown improvements in both models compared to their classical counterparts, with QSVM being the best performing when circuit block entanglement is used. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - machine learning KW - network security LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Quantum machine learning for enhanced intrusion detection TI - Quantum machine learning for enhanced intrusion detection UR - http://hdl.handle.net/11427/43422 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/43422 | |
| dc.identifier.vancouvercitation | Valjee A. Quantum machine learning for enhanced intrusion detection. []. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43422 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | machine learning | |
| dc.subject | network security | |
| dc.title | Quantum machine learning for enhanced intrusion detection | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |