arXiv subjects

Gioliano de Oliveira Braga

Publications and source records attributed to Gioliano de Oliveira Braga.

4 recordsLinked to original sources

arXiv2026

A Proposal for an Agentic AI Architecture to Support Multi-Domain Decision-Making in the Brazilian Armed Forces

The growing complexity of multi-domain operational environments (land, aerospace, naval, cyber, and electromagnetic spectrum) has increased the volume and velocity of data reaching command-and-control (C2) centers, straining the observe-orient-decide-act (OODA) decision cycle. Artificial Intelligence (AI) systems currently employed in defense are, in general, reactive and isolated tools that still rely heavily on human operators to integrate information, assess scenarios, and formulate courses of action. This paper proposes a conceptual Agentic AI architecture for AI systems that can plan, access data sources, execute tools, and act autonomously and audibly, aimed at supporting decision-making across the three Brazilian Armed Forces (Navy, Army, and Air Force). Four application fronts are discussed (decision support, situational analysis, feasibility studies, and countermeasure suggestion), as well as the data and sensor access requirements and the security and permission safeguards necessary for responsible employment across administrative, strategic, operational, and tactical contexts.

cs.AI↗

arXiv2026

ARENA: An Architecture for Measuring the Transferability of Autonomous Cyber Defense

Operational evidence is not automatically scientific evidence. The most realistic Security Operations Center (SOC) data is production telemetry, yet it remains scientifically inaccessible because raw logs cannot be released; as a result, research relies on synthetic or dated datasets. We treat the boundary between private production telemetry and reusable research artifacts as the design object: a methodology that extracts, anonymizes, structures, and validates Security Information and Event Management (SIEM) data from a production financial SOC while preserving task-relevant investigative structure within a declared privacy boundary. Two consumers stress the same artifact. As training material, it fails loudly: 37 MITRE ATT&CK-mapped HIKARI challenges work only when anonymization preserves temporal order and entity consistency. As a measurement substrate, it fails quietly: across 200 SOCpilot incidents, a deterministic verifier detects non-compliant Large Language Model (LLM) actions that are absent from the human baseline. The result is a measurable privacy-utility boundary rather than a formal anonymity claim.

cs.CR↗

arXiv2025

SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Open Challenges

Wi-Fi Channel State Information (CSI) has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational feasibility. However, the field lacks a consolidated understanding of its security properties, adversarial resilience, and methodological consistency. This Systematization of Knowledge (SoK) examines CSI-based biometric authentication through a security lens, analyzing how existing works diverge in sensing infrastructure, signal representations, feature pipelines, learning models, and evaluation methodologies. Our synthesis reveals systemic inconsistencies: reliance on aggregate accuracy metrics, limited reporting of FAR/FRR/EER, absence of per-user risk analysis, and scarce consideration of threat models or adversarial feasibility. To this end, we construct a unified evaluation framework to expose these issues empirically and demonstrate how security-relevant metrics such as per-class EER, Frequency Count of Scores (FCS), and the Gini Coefficient uncover risk concentration that remains hidden under traditional reporting practices. The resulting analysis highlights concrete attack surfaces--including replay, geometric mimicry, and environmental perturbation--and shows how methodological choices materially influence vulnerability profiles. Based on these findings, we articulate the security boundaries of current CSI biometrics and provide guidelines for rigorous evaluation, reproducible experimentation, and future research directions. This SoK offers the security community a structured, evidence-driven reassessment of Wi-Fi CSI biometrics and their suitability as an authentication primitive.

cs.CR↗

arXiv2025

HandPass: A Wi-Fi CSI Palm Authentication Approach for Access Control

Wi-Fi Channel State Information (CSI) has been extensively studied for sensing activities. However, its practical application in user authentication still needs to be explored. This study presents a novel approach to biometric authentication using Wi-Fi Channel State Information (CSI) data for palm recognition. The research delves into utilizing a Raspberry Pi encased in a custom-built box with antenna power reduced to 1dBm, which was used to capture CSI data from the right hands of 20 participants (10 men and 10 women). The dataset was normalized using MinMax scaling to ensure uniformity and accuracy. By focusing on biophysical aspects such as hand size, shape, angular spread between fingers, and finger phalanx lengths, among other characteristics, the study explores how these features affect electromagnetic signals, which are then reflected in Wi-Fi CSI, allowing for precise user identification. Five classification algorithms were evaluated, with the Random Forest classifier achieving an average F1-Score of 99.82\% using 10-fold cross-validation. Amplitude and Phase data were used, with each capture session recording approximately 1000 packets per second in five 5-second intervals for each User. This high accuracy highlights the potential of Wi-Fi CSI in developing robust and reliable user authentication systems based on palm biometric data.

cs.NI↗