arXiv AI August 18, 2026 By Unai Agirre, Imanol Jerico, Felipe Casta\~no, Andrea Venturi, Francesco Zola

A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis

arXiv:2608. 16158v1 Announce Type: cross Abstract: Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

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

efficiency

arXiv AI

Jun 16

Phishing Email Detection Using Large Language Models

arXiv:2512. 10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.

By Najmul Hasan, Prashanth BusiReddyGari, Haitao Zhao, Yihao Ren, Jinsheng Xu, Shaohu Zhang

llms safety

arXiv Machine Learning

Jun 24

A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage

arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.

By Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud

nlp multimodal benchmarks

arXiv AI

Jul 22

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

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.

By Tanveer Ahmed, Seyedali Pourmoafil

llms fine-tuning safety

arXiv Machine Learning

Jun 11

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

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.

By Warren Fernando, Nikos Komninos

arXiv AI

Jul 17

Pretraining Data Can Be Poisoned through Computational Propaganda

arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.

By Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith, David Kohlbrenner, Kyle Lo

llms safety

Related stories

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

Phishing Email Detection Using Large Language Models

A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

Pretraining Data Can Be Poisoned through Computational Propaganda