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.
PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants
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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
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 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.
arXiv AI
Sep 1
GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation
arXiv:2608.29251v1 Announce Type: new Abstract: Privacy protection for live web traffic requires more than detecting private spans. Agent-based privacy protection systems must determine whether an ou...
arXiv AI
Aug 18
Breaking and Defending LLM-Powered Social Media Bot Detection Systems
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 AI
Jun 3
A Robust and Explainable Transformer-Based Framework for Phishing Email Detection
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 AI
Jul 9
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.
arXiv AI
Aug 18
Assessing AI-Generated vs. Human-Authored Spear Phishing SMS Attacks: An Empirical Study
arXiv:2406. 13049v3 Announce Type: replace-cross Abstract: Personalized phishing is difficult to defend against because messages can be tailored to a target's work, interests, and social context.
arXiv Machine Learning
Aug 26
Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate
The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.
arXiv Computation and Language
Aug 24
Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift
The paper introduces a new evaluation setting called scenario‑level out‑of‑distribution (SL‑OOD) detection for SMS and voice phishing, where entire attack scenarios are omitted from training while the label space stays fixed. It shows that high in‑distribution performance does not guarantee robustness to unseen scenarios, attributing this to scenario memorization. The authors propose ECoG, an evidence‑consistent generative framework that uses evidence‑span supervision and a rationale‑label consistency objective, achieving notable improvements in Macro‑F1, reduced prediction‑rationale inconsistency, and higher token‑level overlap with reference evidence.