Behavioral Grammar: Detecting Adaptive Malware via Tiny Language Model Priors and Second-Order Temporal Analysis

• URL: http://arxiv.org/abs/2608.00745v1
• Date: Sat, 01 Aug 2026 16:28:38 GMT
• Title: Behavioral Grammar: Detecting Adaptive Malware via Tiny Language Model Priors and Second-Order Temporal Analysis
• Abstract summary: This paper presents Behavioral Grammar, a detection architecture that treats host runtime behavior as a structured language.<n>Each system event is discretized into an 8-token representation spanning event type, process, argument skeleton, path category, parent process, user, destination, and inter-event timing.<n>We frame these findings within a coevolutionary economics model, arguing that behavior-grammar detection shifts the evasion cost from rule circumvention to distribution matching.
• Score: 1.560152792366037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern endpoint detection systems face a fundamental tension: signature-based approaches are trivially evaded by polymorphic or adaptive threats, while heavy deep-learning models resist auditability and deployment at scale. This paper presents Behavioral Grammar, a detection architecture that treats host runtime behavior as a structured language and learns its grammar with a compact 0.88M-parameter causal Transformer (TinyGPT). Each system event is discretized into an 8-token representation spanning event type, process, argument skeleton, path category, parent process, user, destination, and inter-event timing. The model learns the conditional distribution of normal behavior in a purely self-supervised manner, and anomaly scores are derived from per-slot negative log-likelihood (NLL) statistics, yielding a mathematically bounded false-positive rate. We augment this prior with prototype learning for known-attack attribution, second-order temporal analysis for cadence-based detection, self-learning pattern extraction, and a five-network fusion pipeline. Against an Adaptive Adversarial Agent (AAA)--a threat that learns survival strategies under defensive pressure, performs behavioral mimicry, and matches host event rates--our system achieves 93% detection at 3.84% onboarding false-positive rate. The strongest discriminative signal arises not from any single event but from the coefficient of variation of inter-event intervals: the AAA stepping cadence exhibits CV=0.310 versus 9.786 for benign sleep intervals, a 30x separation reflecting a fundamental stealth-functionality trade-off. We frame these findings within a coevolutionary economics model, arguing that behavior-grammar detection shifts the evasion cost from rule circumvention (cheap) to distribution matching (expensive), establishing a structural asymmetry favoring the defender.

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