TITLE:
Robustness, Cost, and Attack-Surface Concentration in Phishing Detection
AUTHORS:
Julian Allagan, Mohamed Elbakary, Zohreh Safari, Weizheng Gao, Gabrielle Morgan, Essence Morgan, Vladimir Deriglazov
KEYWORDS:
Robustness
JOURNAL NAME:
Journal of Information Security,
Vol.17 No.2,
April
27,
2026
ABSTRACT: Phishing detectors built on engineered website features attain near-perfect accuracy under i.i.d. evaluation, yet deployment security depends on robustness to post-deployment feature manipulation. We study this gap through a cost-aware evasion framework that models discrete, monotone feature edits under explicit attacker budgets. Three diagnostics are introduced: minimal evasion cost (MEC), the evasion survival rate
S(
B
)
, and the robustness concentration index (RCI). On the UCI Phishing Websites benchmark (11,055 instances, 30 ternary features), Logistic Regression, Random Forests, Gradient Boosted Trees, and XGBoost all achieve
AUC≥0.979
under static evaluation. Under budgeted sanitization-style evasion, robustness converges across architectures: the median MEC equals 2 with full features, and over 80% of successful minimal-cost evasions concentrate on three low-cost surface features. Feature restriction improves robustness only when it removes all dominant low-cost transitions. Under strict cost schedules, infrastructure-leaning feature sets exhibit 17% - 19% infeasible mass for ensemble models, while the median MEC among evadable instances remains unchanged. We formalize this convergence: if a positive fraction of correctly detected phishing instances admit evasion through a single feature transition of minimal cost
c
min
, no classifier can raise the corresponding MEC quantile above
c
min
without modifying the feature representation or cost model. Adversarial robustness in phishing detection is governed by feature economics rather than model complexity.