most citedAttacking Slicing Network via Side-channel Reinforcement Learning Attack

3 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2025

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…

quant-ph2025

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…

cs.CR2024

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…

cs.LG20241 cited

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…

cs.CR20243 cited

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…