Sidnei Barbieri

Sidnei Barbieri, Ágney Lopes Roth Ferraz, Lourenço Alves Pereira Júnior

Cybersecurity literature reviews require a reproducible denominator: the set of papers that a protocol includes before screening and synthesis begin. Today, that denominator is often reconstructed from publisher portals, bibliographic indices, and scholarly application programming interfaces (APIs) whose coverage, formats, and query semantics change over time. This paper presents TopVenues, an open-source system that materializes corpus construction as a versioned research artifact. TopVenues declares a venue and year scope, uses DBLP Computer Science Bibliography (DBLP) as the metadata spine, enriches records with abstracts and BibTeX entries via open scholarly APIs and publisher-specific extractors, and stores the results in a monotonic SQLite snapshot, accessible via a command-line interface (CLI), a web interface, and export paths for review workflows. The May 2026 snapshot contains 9,925 papers from 11 cybersecurity sources over 2017 to 2026, with 99.86% abstract coverage and 99.99% BibTeX coverage; keyword search over the full corpus completes in under 31 ms, and a 250-test suite validates the data-integrity invariants. The fixed denominator also enables repeatable measurement: 29.2% of 2024 to 2025 papers from the four top-ranked security conferences in our scope appear as arXiv preprints, with a median of five months before publication, and a prior-author-track-record filter yields a 16.5x precision gain at 90% recall for triaging preprints that later appear in the same venue set. TopVenues links corpus construction to auditable cybersecurity measurement by making the corpus itself executable, inspectable, and citable. The artifact is available at https://github.com/sidneibarbieri/topVenues.

Ágney Lopes Roth Ferraz, Sidnei Barbieri, Murray Evangelista de Souza et al.

Cyber threat intelligence (CTI) encoded in STIX and structured according to the MITRE ATT&CK framework has become a global reference for describing adversary behavior. However, ATT&CK was designed as a descriptive knowledge base rather than a procedural model. We therefore ask whether its structured artifacts contain sufficient behavioral detail to support multi-stage adversary emulation. Through systematic measurements of the ATT&CK Enterprise bundle, we show that campaign objects encode just fragmented slices of behavior. Only 35.6% of techniques appear in at least one campaign, and neither clustering nor sequence analysis reveals any reusable behavioral structure under technique overlap or LCS-based analyses. Intrusion sets cover a broader portion of the technique space, yet omit the procedural semantics required to transform behavioral knowledge into executable chains, including ordering, preconditions, and environmental assumptions. These findings reveal a procedural semantic gap in current CTI standards: they describe what adversaries do, but not exactly how that behavior was operationalized. To assess how far this gap can be bridged in practice, we introduce a three-stage methodology that translates behavioral information from structured CTI into executable steps and makes the necessary environmental assumptions explicit. We demonstrate its viability by instantiating the resulting steps as operations in the MITRE Caldera framework. Case studies of ShadowRay and Soft Cell show that structured CTI can enable the emulation of multi-stage APT campaigns, but only when analyst-supplied parameters and assumptions are explicitly recorded. This, in turn, exposes the precise points at which current standards fail to support automation. Our results clarify the boundary between descriptive and machine-actionable CTI for adversary emulation.

Sidnei Barbieri, Leonardo Vaz de Meneses, Ágney Lopes Roth Ferraz et al.

Security operations centers (SOCs) are beginning to use large language models (LLMs) as copilots to draft incident-response plans. These plans may include actions that are valid per the catalog but still violate mandatory steps, required ordering, or approval gates before analyst review. SOCpilot makes this compliance question measurable at the plan boundary. It fixes the incident package, action catalog, policy rules, verifier, and public evidence surface. Next, it verifies the copilot's proposed action trace. We evaluate two LLM providers on 200 real incidents from an anonymized production SOC in a financial-sector case study. We compare their plans to paired analyst-authored references from the same security orchestration, automation, and response (SOAR) cases. An identical inline policy text moves the two providers in opposite directions. A deterministic verifier removes 466 non-compliant, approval-gated actions, without reducing baseline-task recall. Aggregate rates remain stable across 3 reruns of the fixed corpus. The official evidence focuses on approval-gated decisions regarding recovery and containment. Separately, the artifact exposes zero-cost readiness checks for mandatory and ordering repairs. We release the runnable artifact so independent reviewers can rederive the public results without access to private incident data.

Sidnei Barbieri, Wellington Vargas, Ágney Lopes Roth Ferraz

AI systems increasingly produce investment recommendations, yet the usual evaluations ask the wrong question. Realized return is noisy and easy to overfit, and agreement with a reference portfolio can reward advice that cannot be executed. We argue that an AI-generated recommendation should first be audited as an executable financial action, and only then judged on return. We make this concrete with a deterministic, replayable baseline and a protocol that scores any advisor on three properties a single number conflates: validity under portfolio and fee constraints, stability across repeated runs, and agreement with the baseline. These properties separate cleanly, and agreement is the most misleading in isolation: across a 120-scenario bank, the control that agrees most with the baseline (0.94) is admissible in only 0.58 of its runs, so agreement certifies an invalid action in 42% of them. On an adversarial set, two frontier models are admissible in barely half of their bare-prompt runs and fail on order arithmetic, not judgment; supplying the fee arithmetic deterministically lifts both to near-perfect validity. We make no alpha claim: the baseline is a transparent verifier whose guardrails follow from the fee schedule and whose decisions replay from frozen inputs, and every figure and table regenerates offline from the artifact.

Sidnei Barbieri, Ágney Lopes Roth Ferraz, Lourenço Alves Pereira Júnior

Connecting large language models (LLMs) to defensive enforcement requires more than asking a model whether an attack is happening. A defender must decide which model outputs may change the system state, which outputs must be rejected, and how failures should be recorded. We present PocketAgents, a manifest-driven library of autonomous defense agents. Each agent is installed as three data files: a manifest, a prompt, and a runtime context. The shared runtime gives the agent bounded telemetry access and accepts only typed reports whose requested action appears in the manifest. We implemented PocketAgents on top of a cyber arena (Perry), a cyber-deception testbed, and evaluated two agents, Command and Control and Exfiltration, in 18 closed-loop trials of a DarkSide-inspired attack on a small enterprise topology. Thirteen trials produced validated network-block actions and contained the attack; four failed schema validation; one produced a valid no-action decision. The experiments show that a typed boundary makes LLM-driven defense measurable, extensible, and attributable.

Sidnei Barbieri, Leonardo Vaz de Meneses, Ágney Lopes Roth Ferraz et al.

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.

Sidnei Barbieri, Ágney Lopes Roth Ferraz, Wagner Comin Sonaglio et al.

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.