arXiv:2511. 12085v3 Announce Type: replace-cross Abstract: Phishing and related cyber threats are becoming increasingly sophisticated, with email-based phishing remaining the most persistent attack vector.
By Sajad U P
arXiv:2606. 00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information.
By Uche Unoke Emmanuel, Gideon Francis Oghie
CoGReV is a hybrid framework that enhances machine‑learning phishing classifiers with a post‑hoc, non‑monotonic reasoning layer written in Answer Set Programming. It uses a confidence‑gated defeasible rule to revise low‑confidence phishing predictions toward legitimate only when website metadata is available, thereby allocating uncertain decisions to the reasoning layer while leaving confident ones to the classifier. The gated rule reduces false positives by 0.27 % of decisions and maintains recall within 0.7 % of the baseline, operating in linear time.
By Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti
arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.
By Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud
The paper introduces a Hybrid Fuzzy Ensemble that combines a gradient‑optimized Takagi‑Sugeno‑Kang (TSK) fuzzy classifier with a parallel Fuzzy C‑Means (FCM) module to address the closed‑world gap in anomaly detection on the BETH dataset. It replaces traditional fuzzy membership scores with a robust Min Distance metric to mitigate the curse of dimensionality, achieving a state‑of‑the‑art AUROC of 0.9951 and outperforming Isolation Forest by 17.1%. The approach delivers near‑perfect out‑of‑distribution detection while maintaining interpretability through fuzzy logic.
By Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen
AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection is a multimodal system that evaluates both email content and embedded URLs to detect spam and phishing. It uses a two-layer approach: first, a URL classifier estimates prediction uncertainty, and only messages with high uncertainty are passed to a fine-tuned transformer encoder for deeper semantic analysis. Evaluated on eight diverse training corpora and two real-world datasets covering a decade of attacks, AURA achieves a macro F1-score of 0.9858 in-distribution and maintains scores above 0.94 on the NazPhish-Eval and GuenterTrap-Eval datasets, demonstrating strong generalization to new attack scenarios.
By Omran Berjawi, Walid fahs, Rida Khatoun