The study evaluates quantum machine learning (QML) models for network intrusion detection against well-tuned classical baselines across four standard datasets, using a leakage-controlled protocol and noise simulation. It introduces a quantum-attribution audit to determine whether any performance gains are truly due to quantum effects. While most tuned classical models match or surpass QML, two quantum approaches— a quantum-kernel SVM and a small hybrid circuit—show statistically significant advantages on specific metrics and tasks.
By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui
The paper investigates the role of quantum computing in network‑security classification through two experiments. First, it evaluates near‑term quantum‑kernel support vector machines on datasets such as KDD Cup 1999, CICIDS2017, and BoT‑IoT, finding that quantum kernels can match or sometimes improve classical baselines, though classical RBF kernels often remain stronger. Second, it explores long‑term memory efficiency using quantum oracle sketching (QOS), showing that quantum methods can achieve comparable accuracy with a smaller effective memory footprint than explicit storage, suggesting a potential advantage in memory‑efficient data access for streaming classification tasks.
By Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang, Danindu Gammanpilage, Xin Jin, Zeguan Wu, Junyu Liu
arXiv:2607. 05307v1 Announce Type: cross Abstract: A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal.
By Yewei Yuan, Michele Minervini, Mark M. Wilde, Nana Liu
arXiv:2610.09717v1 Announce Type: cross
Abstract: Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quanti...
By Boaz Micah, Nadia Milazzo, Maissa Beji, Borja Aizpurua, Lloren\c{c} Espinosa-Portal\'es, Esteban Payares, Ghada Ben Slama, Luc Andrea, Michel Kurek, Thomas Cope, Olivier Salomon
The study investigates the vulnerability of a hybrid variational quantum classifier (VQC) used for network intrusion detection to adversarial attacks. By training on the Friday DDoS subset of CICIDS2017, the authors evaluate label‑flipping during training and FGSM evasion at prediction time, comparing results in simulation and on IBM’s Fez superconducting processor (Heron r2). They find that models with similar accuracy can differ markedly in attack resilience: a low‑confidence model loses half its detections to minor input changes, whereas a high‑confidence model requires larger perturbations to fail. Importantly, attacks crafted in simulation transfer to real hardware, and simulated device noise reduces confidence—making attacks easier—without lowering accuracy, indicating that accuracy alone does not reveal a model’s exposure.
By Zeeshan Ajmal, Kimmo Halunen, Marc Mau{\ss}ner, Volker Reers, Arif Ali Khan
arXiv:2610.17027v1 Announce Type: new
Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning...
By Hitanshi Arora, Mohit Swarnkar, Aswani Kumar Cherukuri
arXiv:2608. 04047v1 Announce Type: cross Abstract: Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping.
By Isha, Deepak Singh, Devesh Kumar, S. K Pal, Praful Hambarde, Amit Shukla
arXiv:2607. 13897v1 Announce Type: new Abstract: The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management.
By Abdallah Aaraba, Alexis Vieloszynski, Remon Polus, Ola Ahmad, Soumaya Cherkaoui
The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management. Quantum Kitchen Sinks (QKS) offer a lightweight hybrid quantum feature map suitable for near-term quantum devices, yet their behavior on structured signal data remains poorly understood.
The study evaluates quantum kernel methods for classification against tuned classical kernels, addressing inconsistencies in prior benchmarks by implementing protocol controls such as balanced bandwidth tuning, fold‑specific kernel recomputation, and hardware‑based cost measurement. A factorial design across 324 experimental cells—varying dimensionality, training size, class separability, and data structure—reveals that optimizing the quantum feature map’s bandwidth yields the largest accuracy gains, surpassing any quantum‑classical performance differences observed. Consequently, tuned quantum kernels are statistically indistinguishable from tuned RBF kernels on four real datasets, and the apparent advantages on synthetic data diminish with increasing dimensionality and are largely attributable to protocol variance rather than inherent quantum benefits.
By Ronak Bhalgami
arXiv:2608. 15617v1 Announce Type: new Abstract: Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them.
By Md Rezwanul Islam
arXiv:2607. 20377v1 Announce Type: cross Abstract: Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution.
By Rostyslav Sipakov