26 citations · 35 across the 8 of their papers we have counts for
9 papers
StateFence: Task-State Protection Against Evidence-Chain Contamination in Blue-Team LLM Agents
Haowen Dai, Alejandro Guerra-Manzanares
Indirect prompt injection creates a distinctive risk for blue-team Large Language Model (LLM) agents in Security Operations Centers (SOCs). Malicious instructions can be embedded i…
Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection
Alejandro Guerra-Manzanares, Jialin Huang
Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This…
MIDAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks
Wei Lian, Alejandro Guerra-Manzanares
The rapid expansion of Industrial IoT (IIoT) systems has amplified security challenges, as heterogeneous devices and dynamic traffic patterns increase exposure to sophisticated and…
Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
Xuhui Dou, Hayretdin Bahsi, Alejandro Guerra-Manzanares
Recent work applies Large Language Models (LLMs) to source-code vulnerability detection, but most evaluations still rely on random train-test splits that ignore time and overestima…
MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning
Alejandro Guerra-Manzanares, Farah E. Shamout
The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. Howev…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks
Alejandro Guerra-Manzanares, Farah E. Shamout
Multimodal fusion leverages information across modalities to learn better feature representations with the goal of improving performance in fusion-based tasks. However, multimodal…
Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning
Saeed Shurrab, Alejandro Guerra-Manzanares, Farah E. Shamout
Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. While such approaches deliver promising results, they do not leverage…
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos +1
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due…