16 citations · 31 across the 15 of their papers we have counts for
15 papers
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Bacui Li, Chandra Thapa, Tansu Alpcan +1
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a…
TELSAFE: Security Gap Quantitative Risk Assessment Framework
Sarah Ali Siddiqui, Chandra Thapa, Derui Wang +5
Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effecti…
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
Hevish Cowlessur, Tansu Alpcan, Chandra Thapa +2
Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as qu…
Private Synthetic Data Generation in Bounded Memory
Rayne Holland, Seyit Camtepe, Chandra Thapa +1
We propose , a lightweight synthetic data generator with \textit{differential privacy} guarantees. uses a novel hierarchical decomposition that a…
Computable Model-Independent Bounds for Adversarial Quantum Machine Learning
Bacui Li, Tansu Alpcan, Chandra Thapa +1
By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well-d…
Attacking Slicing Network via Side-channel Reinforcement Learning Attack
Wei Shao, Chandra Thapa, Rayne Holland +2
Network slicing in 5G and the future 6G networks will enable the creation of multiple virtualized networks on a shared physical infrastructure. This innovative approach enables the…
A Hybrid Quantum Neural Network for Split Learning
Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1
Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-c…
One-Shot Collaborative Data Distillation
William Holland, Chandra Thapa, Sarah Ali Siddiqui +2
Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and re…
MTDSense: AI-Based Fingerprinting of Moving Target Defense Techniques in Software-Defined Networking
Tina Moghaddam, Guowei Yang, Chandra Thapa +2
Moving target defenses (MTD) are proactive security techniques that enhance network security by confusing the attacker and limiting their attack window. MTDs have been shown to hav…
Elevating Software Trust: Unveiling and Quantifying the Risk Landscape
Sarah Ali Siddiqui, Chandra Thapa, Rayne Holland +2
Considering the ever-evolving threat landscape and rapid changes in software development, we propose a risk assessment framework called SAFER (Software Analysis Framework for Evalu…
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
Wei Shao, Rongyi Zhu, Cai Yang +7
Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions. Recent advancements have sparked a growin…
Mitigation of Channel Tampering Attacks in Continuous-Variable Quantum Key Distribution
Sebastian P. Kish, Chandra Thapa, Mikhael Sayat +3
Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying…
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Yanqiu Wu, Eromanga Adermann, Chandra Thapa +3
Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation…
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +4
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between…
Transformer-Based Language Models for Software Vulnerability Detection
Chandra Thapa, Seung Ick Jang, Muhammad Ejaz Ahmed +3
The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these mo…