6 citations · 18 across the 24 of their papers we have counts for
10 papers · 1 filter
MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification
Tosin Ige, Christopher Kiekintveld, Aritran Piplai +3
Out of distribution (OOD) detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic…
Empathy by Design: Aligning Large Language Models for Healthcare Dialogue
Emre Umucu, Guillermina Solis, Leon Garza +4
General-purpose large language models (LLMs) have demonstrated remarkable generative and reasoning capabilities but remain limited in healthcare and caregiving applications due to…
Impugan: Learning Conditional Generative Models for Robust Data Imputation
Zalish Mahmud, Anantaa Kotal, Aritran Piplai
Incomplete data are common in real-world applications. Sensors fail, records are inconsistent, and datasets collected from different sources often differ in scale, sampling rate, a…
AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training & Experimentation Scenarios
Ana M. Rodriguez, Jaime Acosta, Anantaa Kotal +1
Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large…
PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction
Leon Garza, Anantaa Kotal, Aritran Piplai +3
Redacting Personally Identifiable Information (PII) from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-…
ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
Md Tanvirul Alam, Aritran Piplai, Nidhi Rastogi
Machine learning models are commonly used for malware classification; however, they suffer from performance degradation over time due to concept drift. Adapting these models to cha…