A novel phishing URL detection system is introduced that employs a first-order Takagi-Sugeno-Kang (TSK) fuzzy inference model optimized via gradient-based techniques, specifically using the Adam optimizer. The framework combines fuzzy logic’s interpretability with gradient optimization’s precision, achieving an average accuracy of 99.95% and a perfect AUC of 1.00 across 5 cross-validation folds on a dataset of over 235,000 URLs. The optimized fuzzy rules and membership functions provide clear decision pathways, enhancing transparency for cybersecurity applications.
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.
arXiv:2606. 00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information.
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.
arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.
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.
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.
arXiv:2512. 10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.
arXiv:2607. 18429v1 Announce Type: cross Abstract: Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them.
arXiv:2606. 11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels.
The paper presents a visual phishing detection system that treats rendered webpages as images, using a YOLOv8 convolutional neural network to classify full-page screenshots as phishing or legitimate. It achieves 92% accuracy, processes a screenshot in about 100 ms, and reduces false positives by 11% after targeted data augmentation. The authors detail dataset creation, augmentation, model architecture, training, and discuss how this visual approach complements existing URL- and content-based defenses.
arXiv:2608. 15893v1 Announce Type: new Abstract: The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms.
arXiv:2610.27675v1 Announce Type: cross Abstract: The emergence of increasingly sophisticated and evasion-resistant phishing attack strategies represents a significant threat to modern cybersecurity,...