Author

Asifullah Khan

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#machine learning Preprint Sep 2026

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...

Aleesha Zainab, Asifullah Khan, M. Khalid et al. · 0 citations

#artificial intelligence Preprint Sep 2026

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

Adaptive HMAS can provide accuracy cost tradeoff for ransomware analysis while retaining support for heterogeneous and incomplete modalities, as well as reducing average analysis cost and average analysis latency.

Mubashar Iqbal, Asifullah Khan · 0 citations

#machine learning Review Aug 2026

Benchmarking Classical and Transformer-Based Models for Document Sensitivity Classification

This paper introduces Strategic 16K, a carefully constructed, leakage-controlled corpus of 16,000 diplomatic cables sourced from the WikiLeaks Public Library of US Diplomacy (PlusD), and presents a systematic benchmark evaluating six model architectures spanning classical machine learning and transformer-based approach...

Aleesha Zainab, M. Khalid, F. Khan et al. · 1 citation