This study analyzes a large dataset of mobile messaging scams from Reddit, finding that rapidly growing reply-based scams are poorly detected by current off-the-shelf tools, necessitating the developm…

The paper introduces RoboKA, a KAN-based multimodal fusion framework, and a synthetic dataset (Robo-SAr) to significantly improve robocall surveillance by modeling complex adversarial interactions bet…

The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

This paper provides a large-scale characterization of Telegram bots, revealing that while they serve useful functions like crowdsourcing, they are also extensively used for malicious activities such a…

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 argues that deepfake detection research is misaligned because it focuses on historical threats (public-figure face-swaps) while ignoring the dominant, emerging harms like NCII, voice-cloning…

The paper proposes a lightweight, passive bot detection system using user-agent and favicon analysis on web server logs, achieving 67.7% bot detection with a low 3% false-positive rate.

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 identifies and characterizes NTP Pool servers that harvest IPv6 client addresses and uses them for reconnaissance, port scanning, and service enumeration.

This cross-national review analyzed government cybersecurity guidance for smart homes, finding that while general security advice is abundant, structured, step-by-step incident response guidance is ra…

The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.

This paper evaluates the effectiveness of interactive and non-interactive bot defense systems against commercial Captcha-solving services and LLM-based browser agents.

GuardSec is a deployable, multi-modal web platform designed for the African context that enables non-technical users to perform real-time digital fraud detection and assess their own connection securi…

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

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 a novel structural invariant approach, derived from the economic constraints of fraud, that amplifies weak, low-precision signals into highly accurate fraud detections without requi…

By analyzing over 27,000 posts from 325 public ransomware leak sites, this paper demonstrates that ransomware groups exhibit non-random, predictable operational regularities concerning victim concentr…

The paper introduces V.O.I.C.E, a novel, empirically grounded risk taxonomy that comprehensively models the diverse privacy, security, and governance risks associated with the unconsented synthesis an…

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 analyzes the real threat of GenAI in cybercrime, arguing that while high-end automation (Stand-Alone Complex) is possible, current adoption is low and primarily affects skilled actors, sugge…