1 citations · 1 across the 10 of their papers we have counts for
11 papers
LSA-PAST: Layer-Specific Adaptation and Prototype Transport for Compositional Incremental Learning
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +2
Class-Incremental Learning (CIL) enables models to recognize new classes over time while retaining prior knowledge, but many real-world problems are compositional, where visual con…
Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems
Kyle Stein, Guillermo Francia, Eman El-Sheikh +1
The growing reliance on Low-Earth Orbit (LEO) satellite communication systems has increased the need for intelligent methods capable of detecting cyberattacks across complex and dy…
Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
Kyle Stein, Guillermo Francia, III Eman El-Sheikh +1
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on ne…
Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify o…
Transductive One-Shot Learning Meet Subspace Decomposition
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
One-shot learning focuses on adapting pretrained models to recognize newly introduced and unseen classes based on a single labeled image. While variations of few-shot and zero-shot…
Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Integrating new class information without losing previously acquired knowledge remains a central challenge in artificial intelligence, often referred to as catastrophic forgetting.…
Visual Adaptive Prompting for Compositional Zero-Shot Learning
Kyle Stein, Arash Mahyari, Guillermo Francia +1
Vision-Language Models (VLMs) have demonstrated impressive multimodal capabilities in learning joint representations of visual and textual data, making them powerful tools for task…
Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia +1
Backdoor attacks pose a critical threat by embedding hidden triggers into inputs, causing models to misclassify them into target labels. While extensive research has focused on mit…
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach
Kyle Stein, Andrew A. Mahyari, Guillermo Francia +1
As the complexity and connectivity of networks increase, the need for novel malware detection approaches becomes imperative. Traditional security defenses are becoming less effecti…
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples
Kyle Stein, Arash Mahyari, Guillermo Francia +1
As networks continue to expand and become more interconnected, the need for novel malware detection methods becomes more pronounced. Traditional security measures are increasingly…
A Transformer-Based Framework for Payload Malware Detection and Classification
Kyle Stein, Arash Mahyari, Guillermo Francia +1
As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as D…