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Sudaroli Dhananjeyan

Publications and source records attributed to Sudaroli Dhananjeyan.

2 recordsLinked to original sources

The Exclusion Ratchet: False-Positive Suppression Accumulates and Persists in Detection Rule Repositories

When a rule produces too many false alarms an analyst adds an exclusion, and the rule thereafter declines to alert in that circumstance. Each such decision is locally reasonable; what becomes of them collectively is not known. Recent longitudinal work established that curation does not converge, but measured restoration time only for revisions that were later reverted -- a measure silent about narrowing that is never undone. We measure that. Across nine years and 8,234 revisions of the SigmaHQ corpus we detect suppression semantically -- growth in the set of predicates held under negation without compensating growth in coverage -- and validate it against blinded hand labelling (precision 0.828, recall 0.911). The test is deterministic: nothing is learned from the data, and the definitions are released as code. Exclusions were added 1,642 times and withdrawn 304, a ratio of 5.4 to 1 that rises to 13 to 1 at the level of the individual rule. Thirty-one per cent of the narrowing is invisible to structural comparison, which existing structural accounts therefore undercount. Estimated by Kaplan-Meier, 86.7 per cent of exclusions remain in force three years on, and persistence is independent of whether the rule is the only coverage for its ATT&CK technique (p = 0.49). Of path-valued exclusions, 64.1 per cent can be satisfied by an unprivileged process that chooses a filename. Narrowing accumulates, is rarely revisited, and is not triaged by consequence. We give a criterion for deciding which exclusions to examine first.

cs.CR↗

Lumina: An AI-Augmented Multiscale Material Informatics Framework for Extreme Aero-Chemo-Thermo-Mechanical Regimes

Predictive simulations and experimental design involving extreme aero-chemo-thermo-mechanical regimes require high-fidelity material representation across diverse physical states. However, data for metals, polymers, and propellants, explosives, and pyrotechnics (PEP) remain fragmented, obstructing traceability for formulators, experimentalists, and simulation engineers. This work introduces Lumina, a modular Python-based informatics framework that centralizes multiscale material data from atomistic simulation datasets to macro-scale experimental records, within a unified repository. Lumina employs a hierarchical XML-based schema and a dynamic runtime parsing mechanism to enable schema-independent parameter extraction. Beyond storage, the platform provides computational modules to visualize model fits, allowing experimentalists to optimize design of experiments (DoE) and formulators to validate chemical behaviors against benchmarks. This structured architecture serves as a high-fidelity pipeline for training machine learning models and enhancing the accuracy of predictive simulations. To streamline multi-disciplinary workflows, Lumina integrates a conversational AI assistant for intelligent material retrieval and natural language querying. By consolidating multiscale data into an extensible ecosystem, Lumina provides a scalable foundation for data-driven discovery and predictive modeling in advanced defense and aerospace engineering.

physics.comp-ph↗