This paper introduces a machine learning system that detects phishing emails by analyzing contextual features from the entire email body content, achieving 95.41% accuracy using Logistic Regression.

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper introduces Smart-SIEM, an AI module for Wazuh that significantly improves web attack detection by incorporating behavioral context vectors and utilizing a hybrid LightGBM/XGBoost cascade.

This paper proposes the first web-focused threat model for agentic browsers, demonstrating that traditional web social engineering attacks can be amplified into dangerous, reproducible threats when ex…

The paper introduces GuardPhish, a large-scale dataset and evaluation framework, demonstrating that even high-performing open-source LLMs can generate actionable phishing content despite accurate inte…

AttackEval systematically evaluates the effectiveness of 250 prompt injection prompts across ten attack categories, finding that composite and obfuscation attacks are highly effective against current…

PhishSigma++ is a novel entity-relation-based detector that improves malicious email detection by focusing on invariant functional relationships between typed entities, significantly outperforming tex…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper introduces musicPIIrate, a novel tool that demonstrates how Offensive AI can infer sensitive user attributes (like age, gender, and personality) from public music playlists, and proposes Jam…

This paper investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…

PHANTOM is a novel framework that generates highly convincing, context-aware honeytokens by incorporating deep organizational knowledge, significantly improving their believability and detection resis…

PIIGuard introduces a novel webpage-level defense mechanism using optimized hidden HTML fragments to prevent LLM assistants from scraping contact-style PII, achieving high defense success rates while…

This study profiles user vulnerability to phishing by identifying key psychological and behavioral factors, revealing that most users are high-risk due to hasty decision-making rather than lacking tec…

CyberCane is a neuro-symbolic framework that enhances phishing detection by combining symbolic rule analysis with privacy-preserving RAG and formal ontology reasoning, achieving high recall against AI…

The paper proposes a graph-based framework for detecting attacks in LLM agent tool-call traffic, finding that content-level embeddings are crucial for high accuracy and that tree ensembles on these em…

The paper introduces ARCANE, a Bayesian network framework for cross-campaign cyber attribution, finding that while aggregating telemetry improves identification, structural feature limitations prevent…

The paper introduces a synthetic dataset of multi-round conversations to detect conversational smishing, finding that XGBoost with TF-IDF features achieved the best performance (72.5% accuracy).

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these honeypots provide substantially longer and harder-to-detect…