54 citations · 69 across the 21 of their papers we have counts for
12 papers · 1 filter
IoTEdu: Access Control, Detection, and Automatic Incident Response in Academic IoT Networks
Joner Assolin, Diego Kreutz, Leandro Bertholdo
The growing presence of IoT devices in academic environments has increased operational complexity and exposed security weaknesses, especially in academic institutions without unifi…
A Taxonomy of Pix Fraud in Brazil: Attack Methodologies, AI-Driven Amplification, and Defensive Strategies
Glener Lanes Pizzolato, Brenda Medeiros Lopes, Claudio Schepke +1
This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classif…
Synthetic Data: AI's New Weapon Against Android Malware
Angelo Gaspar Diniz Nogueira, Kayua Oleques Paim, Hendrio Bragança +2
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective det…
Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments
Marcio Pohlmann, Alex Severo, Gefté Almeida +3
SOCs and CSIRTs face increasing pressure to automate incident categorization, yet the use of cloud-based LLMs introduces costs, latency, and confidentiality risks. We investigate w…
Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen
Anna Luiza Gomes da Silva, Diego Kreutz, Angelo Diniz +2
Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. Thi…
Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMs
Beatriz Machado, Douglas Lautert, Cristhian Kapelinski +1
This paper proposes an automated LLM-based method to extract and structure vulnerabilities from OpenVAS and Tenable WAS scanner reports, converting unstructured data into a standar…
AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizers
Cristhian Kapelinski, Douglas Lautert, Beatriz Machado +1
This work presents AnonLFI 2.0, a modular pseudonymization framework for CSIRTs that uses HMAC SHA256 to generate strong and reversible pseudonyms, preserves XML and JSON structure…
On-Premise SLMs vs. Commercial LLMs: Prompt Engineering and Incident Classification in SOCs and CSIRTs
Gefté Almeida, Marcio Pohlmann, Alex Severo +3
In this study, we evaluate open-source models for security incident classification, comparing them with proprietary models. We utilize a dataset of anonymized real incidents, categ…
MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research
Hendrio Braganca, Diego Kreutz, Vanderson Rocha +2
We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide ran…
Assessing the Impact of Post-Quantum Digital Signature Algorithms on Blockchains
Alison Gonçalves Schemitt, Henrique Fan da Silva, Roben Castagna Lunardi +3
The advent of quantum computing threatens the security of traditional encryption algorithms, motivating the development of post-quantum cryptography (PQC). In 2024, the National In…
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation
Vanderson Rocha, Diego Kreutz, Gabriel Canto +2
Feature selection is vital for building effective predictive models, as it reduces dimensionality and emphasizes key features. However, current research often suffers from limited…
Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz +4
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to s…