arXiv AI
PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants
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arXiv AI
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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.
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.
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.
arXiv AI
Oct 15
Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
The paper evaluates TypeSafe AI’s Jev safety judge against GPT and GLM judges and local classifiers using a controlled adversarial test harness. Jev shows strong robustness on rule‑based tasks like input moderation, with minimal bypasses, but performs less well on judgment‑based tasks such as phishing detection, where it misses a higher proportion of phishing emails and is more prone to false positives. The study highlights Jev’s speed advantage and varying resilience depending on task type.
arXiv AI
Sep 10
CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification
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 Computation and Language
Oct 27
"I Strongly Suspect This Website Is a Scam": Benchmarking PII Leakage and Detection without Defense in Autonomous Web Agents
arXiv:2606.00497v2 Announce Type: replace-cross Abstract: Deceptive web content, widely instantiated across the internet and commonly known as \textit{social-engineering attacks}, manipulates autonom...