[Submitted on 17 Jun 2026 (v1), last revised 29 Jul 2026 (this version, v2)]

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Abstract:Multiple Advanced Persistent Threat (APT) campaigns may execute concurrently, producing audit logs whose events are interleaved without explicit campaign boundaries. We formulate this setting as Unknown-K Interleaved Sequence Demixing (UKISD): recovering coherent campaign episodes when the number of concurrent campaigns is unknown. Each episode is represented as a sequence of MITRE ATT&CK technique occurrences, and demixing is cast as occurrence-level assignment with sequence reconstruction. Existing methods often assume single-campaign observations or rely on local heuristics, limiting robustness to severe interleaving, repeated techniques, abstraction noise, and unknown mixture cardinality.
We propose Topic-Guided Consistency Modeling (TGCM), a consistency-inspired one-step framework for UKISD. TGCM maps an interleaved technique sequence to campaign episodes, jointly performing occurrence assignment and sequence reconstruction in one forward pass. It combines topic guidance from ATT&CK narratives with an embedding-space self-consistency objective to improve semantic coherence and retain efficient inference.
We evaluate TGCM on synthetic mixtures, mixed benchmarks, DARPA engagement traces, and CAPTure, an end-to-end benchmark containing 200 scenarios, 855 million audit events, and 25 ATT&CK-aligned attack profiles in single-host and multi-host settings. TGCM improves occurrence-level assignment under heavy interleaving, repeated technique reuse, symbolic extraction errors, and budgeted unknown-K inference. It also generalizes to unseen benchmarks without retraining and remains effective with practical ATT&CK extraction pipelines. These results establish TGCM as a practical post-abstraction framework for reasoning about concurrent APT campaigns. Code and artifacts are available at this https URL.

Submission history

From: Guo-Wei Wong [view email]
[v1] Wed, 17 Jun 2026 03:35:43 UTC (961 KB)
[v2] Wed, 29 Jul 2026 12:04:49 UTC (1,038 KB)