15 citations · 41 across the 33 of their papers we have counts for
6 papers · 1 filter
Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities
Eric Yocam, Anthony Rizi, Mahesh Kamepalli +3
As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure des…
Causal Interpretability for Adversarial Robustness: A Hybrid Generative Classification Approach
Chunheng Zhao, Pierluigi Pisu, Gurcan Comert +3
Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model predictions. While adversarial tr…
Quantum Annealing-Enhanced Virtual Traffic Lights and its Evaluation Using a Quantum-in-the-Loop Simulation Testbed
Abyad Enan, M Sabbir Salek, Mashrur Chowdhury +3
Virtual Traffic Light (VTL) is a traffic control method that does not require traffic signal-related infrastructure for roadway intersections. Connected vehicles (CVs) are given ri…
Crash Severity Risk Modeling Strategies under Data Imbalance
Abdullah Al Mamun, Abyad Enan, Debbie A. Indah +3
This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) under crash data imbalance between low-sever…
An AutoML-based approach for Network Intrusion Detection
Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert +6
In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML fram…
Robust hardware Trojan detection leveraging dual-domain features and stacked ensemble learning
Sefatun-Noor Puspa, Abyad Enan, Reek Majumdar +3
Cyber-physical systems rely on integrated circuits (ICs), making them vulnerable to hardware Trojans that can remain dormant until triggered, causing functional disruption or infor…