This work proposes an analyst-oriented clustering methodology based on Multi-Criteria Decision Analysis to surface operationally meaningful relationships among phishing messages that bypassed state-of-the-art protections and shows that the resulting Multi-Dimensional Clusters are cohesive, stable, and operationally meaningful.
A novel, hybrid detection framework that constructs a structured “portrait” of each URL by extracting 12 discriminative features-spanning structural, semantic, technical, and morphological dimensions, addressing critical gaps in existing black-box AI systems and aligns with the stringent transparency requirements of se...
S. Buchyk, S. Toliupa, Anastasiia Shabanova et al.· CSDP· 0 citations
Investigating whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse demonstrates that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis...
Unai Agirre, Imanol Jerico, Felipe Castaño et al.· 0 citations
A multi-layer active-web dataset comprising 67,502 scans, including 33,387 phishing observations from operational feeds and 34,115 screened benign reference observations, provides an inspectable and reproducible foundation for future phishing measurement and dataset research.
Furkan Çolhak, Ferhat Demirkıran, Hasan Dağ et al.· 0 citations
This work provides a multi-dimensional framework for evaluating phishing detection systems and demonstrates that for most real-world applications, a multilingual, efficient encoder like XLM-RoBERTa represents the most viable and trustworthy choice.
Armand Florent Tsafack Piugie, Mathieu Valois, Emmanuel Giguet et al.· International Conference on...· 0 citations
This work reframes cross-model phishing detection from a problem of model incompatibility to one of practical calibration, and provides two deployable solutions, threshold recalibration on a small target slice and aggregated-pool training, along with a publicly released multi-LLM corpus.
Rommel Gutierrez, W. Villegas-Ch., Jaime Govea· Frontiers in Big Data· 0 citations
PhishingGAT, a detector that fuses word-level semantic features with structural ones and is hardened against adversarial perturbation, is presented, a detector that fuses word-level semantic features with structural ones and is hardened against adversarial perturbation.
R. Kodali, Siva Rama Krishna T Dr· International Journal of Inn...· 0 citations
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