arXiv subjects

Pedro M. Ferreira

Publications and source records attributed to Pedro M. Ferreira.

15 recordsLinked to original sources

arXiv2026

A Survey on Semantic Modeling for Building Energy Management

Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector. Although IoT technologies now provide extensive operational data, heterogeneous data models, device descriptions, and contextual representations continue to limit semantic interoperability, limiting the development of generalisable, autonomous, context-aware BEM applications. Ontologies address this challenge by providing structured, machine-interpretable representations of building data, systems, and operational context. This survey examines semantic modelling for BEM during the building operational phase. It reviews 60 semantic models and analyses more than 20 ontology-based BEM use cases. It further quantifies Ontology Instantiation Rates (OIR) and missing concepts across those use cases. To support evidence-based assessment of ontology use, we introduce the notion of Ontology Evidence Completeness (OEC), a measure of whether studies explicitly map operational concepts to the ontology classes used to represent them. Findings show that current semantic models more consistently represent physical building structure, technical systems, sensing devices, and observable operational data than abstract and dynamic operational concepts. Concepts such as key performance indicators, assessments, services, control logic, optimisation tasks, and computational workflows remain less consistently covered. Applied BEM studies therefore frequently depend on ontology reuse, integration, specialisation, external inheritance, or application-specific extension to address coverage and interoperability gaps across BEM. By synthesising these patterns, this survey clarifies the capabilities of existing semantic models and identifies directions for more interoperable, generalisable, and context-aware BEM systems.

cs.AI↗

arXiv2026

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems

Cyber Threat Intelligence (CTI) has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a more accurate and rapid understanding of cyber threats. Due to the large volume of data, automation through Machine Learning (ML) and Natural Language Processing (NLP) models is essential for effective CTI extraction. These automated systems leverage Open Source Intelligence (OSINT) from sources like social networks, forums, and blogs to identify Indicators of Compromise (IoCs). Although prior research has focused on adversarial attacks on specific ML models, this study expands the scope by investigating vulnerabilities within various components of the entire CTI pipeline and their susceptibility to adversarial attacks. These vulnerabilities arise because they ingest textual inputs from various open sources, including real and potentially fake content. We analyse three types of attacks against CTI pipelines, including evasion, flooding, and poisoning, and assess their impact on the system's information selection capabilities. Specifically, on fake text generation, the work demonstrates how adversarial text generation techniques can create fake cybersecurity and cybersecurity-like text that misleads classifiers, degrades performance, and disrupts system functionality. The focus is primarily on the evasion attack, as it precedes and enables flooding and poisoning attacks within the CTI pipeline.

cs.CR↗

arXiv2024

Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness

Knowledge sharing about emerging threats is crucial in the rapidly advancing field of cybersecurity and forms the foundation of Cyber Threat Intelligence (CTI). In this context, Large Language Models are becoming increasingly significant in the field of cybersecurity, presenting a wide range of opportunities. This study surveys the performance of ChatGPT, GPT4all, Dolly, Stanford Alpaca, Alpaca-LoRA, Falcon, and Vicuna chatbots in binary classification and Named Entity Recognition (NER) tasks performed using Open Source INTelligence (OSINT). We utilize well-established data collected in previous research from Twitter to assess the competitiveness of these chatbots when compared to specialized models trained for those tasks. In binary classification experiments, Chatbot GPT-4 as a commercial model achieved an acceptable F1 score of 0.94, and the open-source GPT4all model achieved an F1 score of 0.90. However, concerning cybersecurity entity recognition, all evaluated chatbots have limitations and are less effective. This study demonstrates the capability of chatbots for OSINT binary classification and shows that they require further improvement in NER to effectively replace specially trained models. Our results shed light on the limitations of the LLM chatbots when compared to specialized models, and can help researchers improve chatbots technology with the objective to reduce the required effort to integrate machine learning in OSINT-based CTI tools.

cs.CR↗

arXiv2023

New Symmetries of the Two-Higgs-Doublet Model

The Two Higgs Doublet Model invariant under the gauge group SU(2)xU(1) is known to have six additional global discrete or continuous symmetries of its scalar sector. We have discovered regions of parameter space of the model which are basis and renormalization group invariant to all orders of perturbation theory in the scalar and gauge sectors, but correspond to none of the hitherto considered symmetries. We therefore identify seven new symmetries of the model and discuss their phenomenology. Soft symmetry breaking is required for some of these models so that electroweak symmetry breaking can occur. We show that, at least at the two-loop level, it is possible to extend some of these symmetries to include fermions.

hep-ph↗

arXiv2023

Collider phenomenology of new neutral scalars in a flavoured multi-Higgs model

In this work, we propose and explore for the first time a new collider signature of heavy neutral scalars typically found in many distinct classes of multi-Higgs models. This signature, particular relevant in the context of the Large Hadron Collider (LHC) measurements, is based on a topology with two charged leptons and four jets arising from first and second generation quarks. As an important benchmark scenario of the multi-Higgs models, we focus on a recently proposed Branco-Grimus-Lavoura (BGL) type model enhanced with an abelian U(1) flavour symmetry and featuring an additional sector of right-handed neutrinos. We discuss how kinematics of the scalar fields in this model can be used to efficiently separate the signal from the dominant backgrounds and explore the discovery potential of the new heavy scalars in the forthcoming LHC runs. The proposed method can be applied for analysis of statistical significance of heavy scalars' production at the LHC and future colliders in any multi-Higgs model.

hep-ph↗

arXiv2023

Softly broken symmetries in the 2HDM -- an invariant formulation

Soft breaking of a symmetry requires an invariance of the dimension-4 part of the Lagrangian and non-trivial variation of the lower-dimensional part. However, in general, separation between the dim-4 and lower-dimensional Lagrangian is not invariant with respect to basis transformations of fields. Therefore, a natural question of the physical meaning of soft symmetry breaking arises. This problem is addressed here in the framework of two-Higgs-Doublet Models (2HDM). It has been shown, within these models, that in spite of the ambiguity corresponding to the separation between dim-4 and the lower-dimension Lagrangian, implications of the soft symmetry breaking could be formulated in terms of observables, i.e., they are physical and measurable. There are six global symmetries that can be imposed on the scalar sector of the generic 2HDM. Necessary and sufficient tree-level conditions for soft breaking of all of them have been formulated in terms of observables.

hep-ph↗

arXiv2022

Phenomenology of a flavoured multiscalar BGL-like model with three generations of massive neutrinos

In this paper, we present several possible anomaly-free implementations of the Branco-Grimus-Lavoura (BGL) model with two Higgs doublets and one singlet scalar. The model also includes three generations of massive neutrinos that get their mass via a type-I seesaw mechanism. A particular anomaly-free realization, which we dub $ν$BGL-1 scenario, is subjected to an extensive phenomenological analysis, from the perspective of flavour physics and collider phenomenology.

hep-ph↗

arXiv2020

Vacuum structure of the $\mathbb{Z}_2$ symmetric Georgi-Machacek model

We discuss the vacuum structure of a version of the Georgi-Machecek model with an exact $\mathbb{Z}_2$ symmetry acting on the triplet fields. Besides the usual custodial-symmetric model, with $ρ=1$ at tree-level, a model with a dark matter candidate is also viable. The other phases of the model lead to electric charge breaking, a wrong pattern of electroweak symmetry breaking or to $ρ\neq 1$ at tree-level. We derive conditions to have an absolute minimum in each of the two viable phases, the custodial and the dark matter phases.

hep-ph↗

arXiv2020

The Dark Phases of the N2HDM

We discuss the dark phases of the Next-to-2-Higgs Doublet model. The model is an extension of the Standard Model with an extra doublet and an extra singlet that has four distinct CP-conserving phases, three of which provide dark matter candidates. We discuss in detail the vacuum structure of the different phases and the issue of stability at tree-level of each phase. Taking into account the most relevant experimental and theoretical constraints, we found that there are combinations of measurements at the Large Hadron Collider that could single out a specific phase. The measurement of $h_{125} \to γγ$ together with the discovery of a new scalar with specific rates to $τ^+ τ^-$ or $γγ$ could exclude some phases and point to a specific phase.

hep-ph↗

arXiv2019

One-Loop Charge-Breaking Minima in the Two-Higgs Doublet Model

We analyze the vacuum structure of the one-loop effective potential in the two Higgs doublet model. We find that electroweak-breaking vacuua can coexist with charge breaking ones, contradicting a theorem valid at tree-level. We perform a numerical analysis of the model and supply explicit parameter values for which charge-breaking vacuua can be the global minimum of the theory, and deeper than charge-preserving ones.

hep-ph↗

arXiv2019

Processing Tweets for Cybersecurity Threat Awareness

Receiving timely and relevant security information is crucial for maintaining a high-security level on an IT infrastructure. This information can be extracted from Open Source Intelligence published daily by users, security organisations, and researchers. In particular, Twitter has become an information hub for obtaining cutting-edge information about many subjects, including cybersecurity. This work proposes SYNAPSE, a Twitter-based streaming threat monitor that generates a continuously updated summary of the threat landscape related to a monitored infrastructure. Its tweet-processing pipeline is composed of filtering, feature extraction, binary classification, an innovative clustering strategy, and generation of Indicators of Compromise (IoCs). A quantitative evaluation considering all tweets from 80 accounts over more than 8 months (over 195.000 tweets), shows that our approach timely and successfully finds the majority of security-related tweets concerning an example IT infrastructure (true positive rate above 90%), incorrectly selects a small number of tweets as relevant (false positive rate under 10%), and summarises the results to very few IoCs per day. A qualitative evaluation of the IoCs generated by SYNAPSE demonstrates their relevance (based on the CVSS score and the availability of patches or exploits), and timeliness (based on threat disclosure dates from NVD).

cs.CR↗

arXiv2019

Cyberthreat Detection from Twitter using Deep Neural Networks

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

cs.LG↗

arXiv2018

Dimensional emotion recognition using visual and textual cues

This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge). The underlying objective of the challenge is the automatic estimation of emotion expressions in the two-dimensional emotion representation space (i.e., arousal and valence). The adopted methodology is a weighted ensemble of several models from both video and text modalities. For video-based recognition, two different types of visual cues (i.e., face and facial landmarks) were considered to feed a multi-input deep neural network. Regarding the text modality, a sequential model based on a simple recurrent architecture was implemented. In addition, we also introduce a model based on high-level features in order to embed domain knowledge in the learning process. Experimental results on the OMG-Emotion validation set demonstrate the effectiveness of the implemented ensemble model as it clearly outperforms the current baseline methods.

cs.AI↗

arXiv2018

$pp\to A\to Zh$ and the wrong-sign limit of the Two-Higgs-Doublet Model

We point out the importance of the decay channels $A\to Zh$ and $H\to VV$ in the wrong-sign limit of the Two-Higgs-Doublet Model (2HDM) of type II. They can be the dominant decay modes at moderate values of $\tanβ$, even if the (pseudo)scalar mass is above the threshold where the decay into a pair of top quarks is kinematically open. Accordingly, large cross sections $pp\to A\to Zh$ and $pp\to H\to VV$ are obtained and currently probed by the LHC experiments, yielding conclusive statements about the remaining parameter space of the wrong-sign limit. In addition, mild excesses - as recently found in the ATLAS analysis $b\bar b \to A\to Zh$ - could be explained. The wrong-sign limit makes other important testable predictions for the light Higgs boson couplings.

hep-ph↗

arXiv2017

High scale impact in alignment and decoupling in two-Higgs doublet models

The two-Higgs doublet model (2HDM) provides an excellent benchmark to study physics beyond the Standard Model (SM). In this work we discuss how the behaviour of the model at high energy scales causes it to have a scalar with properties very similar to those of the SM -- which means the 2HDM can be seen to naturally favor a decoupling or alignment limit. For a type II 2HDM, we show that requiring the model to be theoretically valid up to a scale of 1 TeV, by studying the renormalization group equations (RGE) of the parameters of the model, causes a significant reduction in the allowed magnitude of the quartic couplings. This, combined with $B$-physics bounds, forces the model to be naturally decoupled. As a consequence, any non-decoupling limits in type II, like the wrong-sign scenario, are excluded. On the contrary, even with the very constraining limits for the Higgs couplings from the LHC, the type I model can deviate substantially from alignment. An RGE analysis similar to that made for type II shows, however, that requiring a single scalar to be heavier than about 500 GeV would be sufficient for the model to be decoupled. Finally, we show that not only a 2HDM where the lightest of the CP-even scalars is the 125 GeV one does not require new physics to be stable up to the Planck scale but this is also true when the heavy CP-even Higgs is the 125 GeV and the theory has no decoupling limit for the type I model.

hep-ph↗