Saeefa Rubaiyet Nowmi

Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon et al.

Quantum Machine Learning (QML) integrates quantum computational principles into learning algorithms, offering improved representational capacity and computational efficiency. However, the security and robustness of QML systems remain underexplored, particularly under adversarial conditions. We present the first comprehensive systematization of adversarial robustness in QML, combining conceptual organization with empirical evaluation across black-box, gray-box, and white-box threat models. We implement five representative attacks: a label-flipping poisoning attack under black-box; an encoder-level indiscriminate poisoning attack and a proxy-model clean-label backdoor attack under gray-box; and a circuit-level backdoor attack (QTrojan) and gradient-based evasion attacks (FGSM and PGD) under white-box. We evaluate these attacks using a Quantum Multilayer Perceptron (QMLP) trained on MNIST and AZ-Class across circuit depths of 2, 5, 10, and 50 layers with angle and amplitude encoding schemes. Our evaluations reveal a fundamental accuracy-robustness trade-off. Amplitude encoding achieves the highest clean accuracy (92.6% on MNIST and 67% on AZ-Class) but collapses under adversarial perturbations and depolarizing noise, whereas shallow angle-encoded models remain more stable. QUID is effective under noiseless conditions but weakened by noise, while the proxy-model backdoor persists unless the circuit itself is overwhelmed. Furthermore, the circuit-level backdoor fails in the multi-class setting, indicating a scalability limitation. Finally, QMLP models are more robust than Classical Multi-Layer Perceptron (CMLP) models under label-flipping attacks but substantially more vulnerable to gradient-based evasion. We conclude by proposing a threat-aware and noise-resilient framework for secure QML deployment.

Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi et al.

Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware naturally exhibits all of these complexities, but for the same reason, it is challenging to curate and organize data to study these factors. We present McNdroid, to our knowledge the largest longitudinal multimodal Android malware benchmark for malware detection and drift analysis. McNdroid spans 2013--2025, excluding 2015, and represents each application with three aligned modalities--static features from manifests and smali code, dynamic behavioral features from sandbox execution, and graph-based features from function-call graphs. Using temporally separated splits, we evaluate standard ML and deep-learning detectors across increasing train--test time gaps. Results show clear temporal degradation, while multimodal fusion outperforms the best single modality across long-term temporal gaps. Cross-modal agreement also declines over time, suggesting that drift affects both individual feature spaces and the consistency among modalities. We further analyze modality-specific drift, malware-family evolution, and temporal changes in model explanations. We publicly release McNdroid, benchmark splits, and code to support reproducible research on temporal generalization and robust multimodal learning in security-critical, non-stationary settings.

Jesus Lopez, Saeefa Rubaiyet Nowmi, Viviana Cadena et al.

Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantum-classical models -- a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data re-uploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -- API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification -- 95-96% on API-Graph, 91-92% on AZ-Domain, and 77% on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.