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: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:2512. 10104v2 Announce Type: cross Abstract: Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion.
arXiv:2606. 21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat.
AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection is a multimodal system that evaluates both email content and embedded URLs to detect spam and phishing. It uses a two-layer approach: first, a URL classifier estimates prediction uncertainty, and only messages with high uncertainty are passed to a fine-tuned transformer encoder for deeper semantic analysis. Evaluated on eight diverse training corpora and two real-world datasets covering a decade of attacks, AURA achieves a macro F1-score of 0.9858 in-distribution and maintains scores above 0.94 on the NazPhish-Eval and GuenterTrap-Eval datasets, demonstrating strong generalization to new attack scenarios.
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:2606. 00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information.
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.
CogniDir is an adaptive distributional learning framework designed to improve fake news detection against new psychologically grounded malicious comments generated by Large Language Models. It reframes robust detection as a dynamic data mixture optimization problem, using cognitive psychology to formalize adversarial paradigms and an information‑theoretic score to guide adaptive sampling of training data. Experiments on three benchmarks show that CogniDir achieves state‑of‑the‑art robustness, boosting F1 scores by up to 17.9% over existing baselines under heterogeneous AI‑generated attacks.
The paper introduces a prompt‑injection detection framework for email assistants that models attacks as a chain of stages. It combines a text detector, stage‑specific verifiers, rule‑based risk signals, user intent consistency checks, and a logistic decision policy. Experiments on five benchmarks show the framework outperforms pretrained detectors, achieving a mean F1 of 0.406 versus 0.216, and demonstrate that training on benign emails resembling attacks reduces false alarms.
Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular security datasets (phishing URLs, UNSW-NB15, NF-ToN-IoT, HIKARI-2021), evaluating five attacks including three black-box methods applicable to non-differentiable tree models.
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. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales.
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.