arXiv subjects

Lucas Miranda

Publications and source records attributed to Lucas Miranda.

3 recordsLinked to original sources

arXiv2026

An Approach to Generate Attack Graphs with a Case Study on Siemens PCS7 Blueprint for Water Treatment Plants

Assessing the security posture of Industrial Control Systems (ICS) is critical for protecting essential infrastructure. However, the complexity and scale of these environments make it challenging to identify and prioritize potential attack paths. This paper introduces a semi-automated approach for generating attack graphs in ICS environments to visualize and analyze multi-step attack scenarios. Our methodology integrates network topology information with vulnerability data to construct a model of the system. This model is then processed by a stateful traversal algorithm to identify potential exploit chains based on preconditions and consequences. We present a case study applying the proposed framework to the Siemens PCS7 Cybersecurity Blueprint for Water Treatment Plants. The results demonstrate the framework's ability to simulate different attack scenarios, including those originating from known CVEs and potential device misconfigurations. We show how a single point of failure can compromise network segmentation and how patching a critical vulnerability can protect an entire security zone, providing actionable insights for risk mitigation.

cs.CR↗

arXiv2024

A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences

The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare.

cs.CL↗

arXiv2020

Functional MRI applications for psychiatric disease subtyping: a review

Psychiatric disorders have historically been classified using symptom information alone. With the advent of new technologies that allowed researchers to investigate brain mechanisms more directly, interest in the mechanistic rationale behind defined pathologies and aetiology redefinition has greatly increased. This is particularly appealing for the field of personalised medicine, which searches for data-driven approaches to improve individual diagnosis, prognosis and treatment selection. Here we intend to systematically analyse the usage of functional MRI on both the elucidation of psychiatric disease biotypes and the interpretation of subtypes obtained via unsupervised learning applied to symptom or biomarker data. We searched the existing literature for functional MRI applications to the obtention or interpretation of psychiatric disease subtypes. The PRISMA guidelines were applied to filter the retrieved studies, and the active learning framework ASReviews was applied for article prioritization. From the 20 studies that met the inclusion criteria, 5 used functional MRI data to interpret symptom-derived disease clusters, 4 used it for the interpretation of clusters derived from biomarker data other than fMRI itself, and 11 applied clustering to fMRI directly. Major depression disorder and schizophrenia were the two most studied pathologies, followed by ADHD, psychosis, autism disorder, and early violence. No trans-diagnostic studies were retrieved. While interest in personalised medicine and data-driven disease subtyping is on the rise and psychiatry is not the exception, unsupervised analyses of functional MRI data are inconsistent to date, and much remains to be done in terms of gathering and centralising data, standardising pipelines and model validation, and method refinement. The usage of fMRI in the field of trans-diagnostic psychiatry remains vastly unexplored.

q-bio.NC↗