arXiv AI September 10, 2026 By Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 31

Cybersecurity Detection Classification with Reasoning-enabled Language Models

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By Amol Khanna, Manu Nandan, Cristian Viorel Popa, Joan Pujol-Roig, Diana Bolocan, Laura Vasilie, Alexandru Apostu, Chase Helwig, Mihaela Gaman, Michael Brautbar, Edward Raff, Chase Midler, Sven Krasser

llms reinforcement-learning

Hugging Face Trending Papers

Jul 30

Cybersecurity Detection Classification with Reasoning-enabled Language Models

A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day. Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but does not train them to reason about whether a detection is a genuine threat.

llms reinforcement-learning

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

Oct 19

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

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.

By Lohith Srikanth Pentapalli, Jon Salisbury, Josette Riep, Kelly Cohen

safety

arXiv AI

Sep 10

PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants

arXiv:2507.15393v2 Announce Type: replace-cross Abstract: Phishing email is a critical step in the cybercrime kill chain due to the high reachability of victims' email accounts and the low cost of la...

By Ruofan Liu, Yun Lin, Yuxin Wang, Xiwen Teoh, Zhenkai Liang, Gongshen Liu, Haojin Zhu, Jin Song Dong

llms rag 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

One email a morning, machine-written

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