activity
20232026
most citedPrivacy-preserving machine learning for healthcare: open challenges and future perspectives

26 citations · 35 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…