QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems

Md Sajedul Karim Chy; Salman Mohammad Abdullah; Mahbub Ahmed Nabil; Abidul Alam; Md Himeluzzaman; Tofayel Ahmed Onik; Shaown Mahamud Shakil; Hafiz Aziz Khan; Md Sajedul Karim Chy; Salman Mohammad Abdullah; Mahbub Ahmed Nabil; Abidul Alam; Md Himeluzzaman; Tofayel Ahmed Onik; Shaown Mahamud Shakil; Hafiz Aziz Khan

doi:10.15662/IJFIST.2022.0505009

Articles

QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems

Md Sajedul Karim Chy

M.S. in Project Management, Washington University of Science and Technology, VA, USA

Salman Mohammad Abdullah

M.S. in Information Technology (IT), Washington University of Science and Technology, VA, USA

Mahbub Ahmed Nabil

Master of Science in Information Technology, Washington University of Science and Technology, VA, USA

Abidul Alam

Bachelor of Business Administration (BBA), International University of Business, Agriculture and Technology, Bangladesh

Md Himeluzzaman

M.Sc. in Information Technology Systems and Management, Washington University of Science and Technology, VA, USA

Tofayel Ahmed Onik

Bachelor of Science in Electrical & Electronics Engineering, Metropolitan University, Sylhet, Bangladesh

Shaown Mahamud Shakil

Bachelor in Human Resource Management, Independent University, Bangladesh

Hafiz Aziz Khan

Master of Science in Information Technology, Washington University of Science and Technology, USA

Abstract

: Machine-learning intrusion detection systems are regularly benchmarked against attack classes that also exist in their training data, a practice that inflates preparedness against the new types of threats most pertinent to national-security networks. We introduce QShield-NS, a variance-reduced quantum machine learning classifier capable of zero-day threat detection, and demonstrate its ability via strict Leave-One-Attack-Out (LOAO) evaluation on the UNSW-NB15 dataset, where each of nine different attack families are omitted from training in turn. Eight principal components are angle-encoded on eight qubits, processed by strongly entangling variational layers and read through Pauli-Z expectation values into a compact classical head that is trained end-to-end. QShield-NS is compared against a multilayer perceptron, a random forest and XGBoost on the same folds, with the same features and decision threshold. A rank inversion is what the results reveal. If however the training is known-attack control split QShield-NS ranks last (macro-F1 0.8793 v s 0.9158 random forest), and under the zero-day protocol first (0.7303 against 0.7044). Its generalisation gap, at 0.1489 to 0.2084–0.2302, is the smallest for the four models and it lowered the mean false-positive rate from 20.24 percent to 10.72 percent, scoring precision on eight of nine folds. It is a calibration, not discrimination advantage: the classical baselines get better ROC-AUC and PR-AUC in every fold but achieve distribution shift recall at the lower price of over-predicting the attack class. A depth ablation and a study of gradient-variance establish robust trainability up to 8 qubits. We do not claim any quantum advantage; all results were obtained by classical simulation

Article Information

Journal	International Journal of Future Innovative Science and Technology (IJFIST)
Volume (Issue)	Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST)
DOI	https://doi.org/10.15662/IJFIST.2022.0505009
Pages	9266-9283
Published	October 10, 2022
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Md Sajedul Karim Chy, Salman Mohammad Abdullah, Mahbub Ahmed Nabil, Abidul Alam, Md Himeluzzaman, Tofayel Ahmed Onik, Shaown Mahamud Shakil, Hafiz Aziz Khan (2022). QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 9266-9283. https://doi.org/10.15662/IJFIST.2022.0505009

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