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Divyangkumar Patel

Publications and source records attributed to Divyangkumar Patel.

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A Case-Control Measurement Study of OSINT Source Effectiveness for Critical Infrastructure Defense

Defenders of critical infrastructure (CI) subscribe to many public open-source intelligence (OSINT) feeds without an empirical basis for which feeds actually precede attacks. We provide one. Across 54 confirmed CI cyberattacks from 2010 through 2024 spanning twelve named CI sectors plus a cross-sector category (consolidation rules in Section IV), paired with 12 null-control vulnerability cases drawn from the same source space, we audit per-source attack coverage, null-case contamination, and signal lead time for ten public OSINT source classes that meet a minimum-volume threshold. Sources separate cleanly into three operationally distinct mission profiles (pooled Fisher exact p = 3.4x10^-8): precursor (six classes with zero observed null firings at coverage at or above 5%), disclosure-exposure (three classes whose null contamination meets or exceeds attack coverage), and one large broad-coverage class that mixes the two profiles but retains 91.3% within-corpus precision. The precision-side classification is stable across a 2019 temporal partition and across a US-versus-non-US geographic partition. Two sources, one broad-coverage and one precursor, cover 92.6% of corpus attacks; three cover 96.3%. The greedy portfolio at k = 3 outperforms the mean random three-source subset by 39.8 percentage points. Several source classes widely treated as canonical for industrial control system defense fall into the disclosure-exposure profile by operational mission, not by quality. Per-sector, per-actor, and per-jurisdiction portfolios diverge in rank order despite a shared rank-one source. The corpus, linkage protocol, and classification rules are released.

cs.CR

Structural Inference in Undocumented Mobile Databases: A Reproducible Benchmark for Evaluating Agentic Reasoning in Digital Forensics

Agentic large language models are increasingly used in digital forensic analysis, yet their ability to infer relational structure inside undocumented mobile application databases remains poorly understood. In forensic contexts, structurally incorrect inferences can yield results that appear plausible while remaining evidentially unsound. This work evaluates agentic structural inference as an isolated capability, treating execution success and structural correctness as distinct evaluation axes. It examines how an agent reconstructs table relationships, linking attributes, and executable join paths when given only a raw database and a natural-language investigative prompt. We apply a fixed, deterministic evaluation pipeline to two contrasting SQLite repositories: Android's SMS database with stable identifier propagation, and Snapchat's database with irregular schemas, ephemeral identifiers, and polymorphic relationships. Using expert-verified SQL ground truth, we evaluate (i) structural correctness of inferred relational links, (ii) execution coherence under multi-table reasoning, and (iii) robustness and failure modes of inferred structure when execution succeeds but relational interpretation diverges from expert ground truth. Evaluation is performed independently of semantic interpretation, with full queries and execution traces provided in the Appendix. Results show that structural inference remains reliable in regular schemas but degrades sharply as schema ambiguity increases, frequently producing structurally plausible yet incorrect joins that execute successfully. These findings clarify where schema-agnostic agentic reasoning can support forensic analysis, how its robustness degrades under realistic schema irregularities, and why additional verification remains essential before inferred relationships can be treated as reliable evidence.

cs.CR