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arXiv:2609.22212v3cs.CL 등록 2026-11-10

A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification

Organizations in critical national infrastructure sectors must assess heterogeneous documents for sensitivity before routing or storage. Manual assessment is slow, inconsistent, and unscalable. Extending our prior leakage-controlled benchmark, BERT established the top single-encoder baseline (89.14% accuracy, 89.33% F1-score under 5-fold cross-validation on the Strategic 16K corpus). However, transformer baselines suffer from a structural limitation: fixed input length truncation discards evidence beyond the retained window-precisely where sensitive cables tend to be longest. We present Channel-Boosted MAS (CB-MAS) and instantiate it as IC-MAS (Iterative Consultation Multi-Agent System) to solve this without long-context computational costs. A Channel Critic Agent learns document-adaptive trust weights governing Gated Channel Boosting between two first-window encoders, while paired Consultation Agents iteratively exchange belief states to reconcile evidence from the beginning and end of long documents. IC-MAS holds computation constant regardless of document length by reconciling fixed windows in a compact representation space. Ablation studies show critic-controlled Channel Boosting provides the bulk of accuracy gains, while consultation recovers recall without precision collapse. Critic-Controlled Gated Channel Boosting with Max-Pool fusion and Blackboard Adaptive Consultation achieves 90.72% accuracy, 91.23% F1-score, 92.01% sensitive recall, and 90.46% sensitive precision, using about 54% less average computation than a fixed-round baseline. Gains over the single-encoder baseline are statistically significant (McNemar's test, p less than 0.000001; paired t-test). We include LIME/SHAP explainability, multi-agent evaluation, and an honest accounting of limitations.

arXiv 원문 상세

arXiv:2609.04820v3cs.CR 등록 2026-11-03

Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

Sandbox execution and memory forensics are among the most resources intensive stages of malware triage. Static analysis can scale to millions of files while dynamic and memory analysis require minutes per sample. Because multimodal ransomware detectors often apply every modality to every sample, costs and latency rise even when static evidence suffice.We proposea cost aware Hierarchical Multi Agent System (HMAS) that frames evidence acquisition as a budgeted sequential decision problem enabling timely ransomware detection. Specialist agents generate schema validated risk signals for each modality while domain controllers aggregate evidence and coordinate analysis. The orchestrator starts with static evidence and escalates only if confidence is low or signals disagree. A locally hosted language model provides a limited, accoutable review without replacing the deterministic pipeline. Experiments on 16 ransomware families show 94.12% binary accuracy (F1 0.9376, ROC-AUC 0.9989). For multi class family attribution, it achieves 98.90% accuracy (macro-F1 0.9748). The system resolves 57.95% of the samples using static analysis alone whhile only 6.21% require the full pipeline. Compared with exhaustive cost of 12.0 units,the proposed system reduces te average cost to 6.724 units, representing approximately 44% saving. Zero day experiments further examine generalization to unseen ransomware families. Additional evaluations examine cost sensitivity and component level contribution. LLM-assisted review provides only marginal improvement in F1 while introducing additional computational overhead. Overall, the results show the potential of adaptive hierarchical multi agent orchestration to balance ransomware detection performance, family attribution analysis cost and decision latency.

arXiv 원문 상세