arXiv subjects

Ricardo Lopes

Publications and source records attributed to Ricardo Lopes.

4 recordsLinked to original sources

arXiv2026

Orchestrated Vulnerability Management for Heterogeneous Networks: Adaptive Two-Stage Vulnerability Assessment, Context-Aware Risk Prioritization, and Automated Mitigation

Heterogeneous networks pose significant security challenges due to device diversity, fragile operating conditions, and heterogeneous firmware and service configurations. Traditional vulnerability management often relies on static scanning and severity-based prioritization, overlooking exploitation likelihood and asset context. This can delay mitigation and increase operational overhead. This paper proposes a SOAR-orchestrated vulnerability management framework integrating passive asset discovery, adaptive two-stage vulnerability assessment, context-aware risk assessment, and automated SDN-based mitigation. The detection engine progressively characterizes device attack surfaces using assessment strategies tailored to device capabilities, minimizing disruption to resource-constrained IoT assets. Risk assessment combines CVSS severity, EPSS exploitation probability, and contextual attributes to prioritize vulnerabilities by operational risk. Based on risk bands, mitigation is automatically enforced through coordinated OpenFlow and IDS policies, ranging from monitoring and selective service isolation to complete host quarantine. Experimental results demonstrate the framework's effectiveness. Adaptive two-stage assessment reduces scan time by up to 91% while identifying 71% of baseline vulnerabilities during the initial stage before selectively triggering further analysis. The context-aware risk model reduces vulnerabilities requiring immediate mitigation by approximately 75% without missing any vulnerability with verified exploitation. Compared with conventional assessment, the framework reduces assessment time for 32 physical hosts by up to 45% and enforces mitigation within milliseconds, enabling efficient and scalable vulnerability management through adaptive assessment, context-aware prioritization, and automated mitigation.

cs.NI↗

arXiv2024

Gl\'orIA -- A Generative and Open Large Language Model for Portuguese

Significant strides have been made in natural language tasks, largely attributed to the emergence of powerful large language models (LLMs). These models, pre-trained on extensive and diverse corpora, have become increasingly capable of comprehending the intricacies of language. Despite the abundance of LLMs for many high-resource languages, the availability of such models remains limited for European Portuguese. We introduce Gl\'orIA, a robust European Portuguese decoder LLM. To pre-train Gl\'orIA, we assembled a comprehensive PT-PT text corpus comprising 35 billion tokens from various sources. We present our pre-training methodology, followed by an assessment of the model's effectiveness on multiple downstream tasks. Additionally, to evaluate our models' language modeling capabilities, we introduce CALAME-PT (Context-Aware LAnguage Modeling Evaluation for Portuguese), the first Portuguese zero-shot language-modeling benchmark. Evaluation shows that Gl\'orIA significantly outperforms existing open PT decoder models in language modeling and that it can generate sound, knowledge-rich, and coherent PT-PT text. The model also exhibits strong potential for various downstream tasks.

cs.CL↗

arXiv2023

On the conformance of Android applications with children's data protection regulations and safeguarding guidelines

With the rapid development of online technologies and the widespread usage of mobile phones among children, it is crucial to protect their online safety. Some studies reported that online abuse and incidents negatively affect children's mental health and development. In this paper, we examine how Android applications follow the rules related to children's data protection in the EU General Data Protection Regulation (GDPR) and the UK and EU children's online safeguarding guidelines. Our findings show that the number of non-compliant apps is still significant. Even the apps designed for children do not always comply with legislation or guidance. This lack of compliance could contribute to creating a path to causing physical or mental harm to children. We then discuss the relevance of automating the compliance verification and online safety risk assessment, including open questions, challenges, possible approaches, and directions.

cs.CY↗

arXiv2011

A Design and Implementation of the Extended Andorra Model

Logic programming provides a high-level view of programming, giving implementers a vast latitude into what techniques to explore to achieve the best performance for logic programs. Towards obtaining maximum performance, one of the holy grails of logic programming has been to design computational models that could be executed efficiently and that would allow both for a reduction of the search space and for exploiting all the available parallelism in the application. These goals have motivated the design of the Extended Andorra Model, a model where goals that do not constrain non-deterministic goals can execute first. In this work we present and evaluate the Basic design for Extended Andorra Model (BEAM), a system that builds upon David H. D. Warren's original EAM with Implicit Control. We provide a complete description and implementation of the BEAM System as a set of rewrite and control rules. We present the major data structures and execution algorithms that are required for efficient execution, and evaluate system performance. A detailed performance study of our system is included. Our results show that the system achieves acceptable base performance, and that a number of applications benefit from the advanced search inherent to the EAM.

cs.PL↗