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…

GESR introduces a graph-based framework that reconstructs edge semantics from local structural context to detect stealthy malicious communications using only benign training data, achieving high perfo…

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 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 proposes TAGBD, a graph-aware backdoor attack that demonstrates that inconspicuous poison text alone can reliably compromise text-attributed graph learning systems.

This paper critically re-evaluates the use of Graph Neural Networks (GNNs) for Bitcoin fraud detection, demonstrating that under strict, leakage-free temporal evaluation, simple feature-only models si…

CivicShield introduces a novel, seven-layered defense-in-depth framework that significantly enhances the security of government-facing AI chatbots against sophisticated multi-turn adversarial attacks.

The paper proposes VulGNN, a lightweight Graph Neural Network (GNN) model, which achieves vulnerability detection performance comparable to large language models (LLMs) while being significantly small…

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.

The paper proposes PROVFUSION, a multi-view fusion framework that integrates anomaly signals from attribute, structure, and causality views to overcome the limitations of single node- or edge-centric…

The paper introduces Auto-Prov, an end-to-end framework that uses Large Language Models (LLMs) to automatically construct functional-embedded provenance graphs from diverse logs, enhancing anomaly det…

This paper provides a comprehensive, structured list of 42 email-based deception techniques, complete with 64 concrete examples, to serve as a modular reference for developing countermeasures.

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…

This paper introduces an attribution-driven analysis of encoder-based Large Language Models (LLMs) for network intrusion detection, demonstrating that the models make decisions based on meaningful tra…

The paper proposes reframing mechanistic anomaly detection (MAD) as a functional attribution problem, using influence functions to measure how much a model's output depends on specific input samples,…

The paper introduces BRIDGE, a standardized benchmark for cross-domain IoT botnet detection, and TCH-Net, a novel multi-branch network that achieves state-of-the-art generalization performance across…

The paper introduces Auto-ART, a comprehensive open-source framework that provides structured meta-analysis and automated testing for adversarial robustness, revealing significant gaps in current ML s…

The paper introduces Gammaf, an open-source benchmarking framework designed to standardize the evaluation of graph-based anomaly detection methods for securing Large Language Model Multi-Agent Systems…