BioHARP: A Feasibility Framework Toward Bio-Adaptive Human Risk Profiling for Phishing with Cost-Sensitive Learning and Scenario-Based Physiological Fusion Design
Şeydanur Ahi Duman, Rukiye Hayran, İbrahim Soğukpınar · Applied Sciences · 2026
Phishing susceptibility reflects both stable psychological traits and transient user states, but confirmed victim cases remain rare in survey studies. This study evaluated BioHARP, a feasibility framework that pairs an outcome-independent psychometric prior with a prospective bio-adaptive fusion design. Using N=136 anonymized respondents (12 strict victims), we constructed 69 pre-incident predictors after excluding administrative metadata, exposure indicators, and post-incident response items. A cost-sensitive TabTransformer was trained without synthetic minority generation and benchmarked against six conventional tabular baselines and FT-Transformer under identical splits, unified preprocessing, and model-appropriate cost-sensitive imbalance handling. Out-of-sample performance was primarily assessed with a 60-seed repeated stratified hold-out protocol with fixed four-positive/thirty-negative test composition. Across the sixty splits, TabTransformer yielded a mean AUC of 0.534±0.157, whereas CatBoost yielded 0.736±0.108. On fixed Seed 100, TabTransformer reached AUC =0.8167 and CatBoost AUC =0.775; for the single-init TabTransformer, this was the best-observed split and was therefore interpreted as an optimistic upper-end point estimate. Threshold-dependent metrics were reported separately as an exploratory analysis with explicit leakage labeling. The physiological fusion layer was evaluated as an outcome-informed oracle upper bound, reaching AUC =0.944 on Seed 100 and 0.878±0.058, range [0.73, 0.98], across 70 alternative scenario RNG seeds. This result was interpreted strictly as theoretical headroom rather than deployment-calibrated performance. Overall, BioHARP was framed as a feasibility framework with a clearly bounded physiological-fusion design and explicit calibration and sensor requirements for future deployment-ready bio-adaptive detectors.