8 papers
The Anatomy of Scam Scenarios: Large-Scale Characterization and Conversation-Aware Detection
Shang Ma, Chen Yanai, Avichai Ben +3
Online scams have become a pervasive global threat, causing substantial financial, psychological, and operational harm. Scammers embed psychological techniques (PTs) within reusabl…
GRID: Graph Representation of Intelligence Data for Security Text Knowledge Graph Construction
Liangyi Huang, Zichen Liu, Fei Shao +5
Security knowledge graphs can provide computable external memory for security agents, but constructing them from long-form cyber threat intelligence (CTI) remains difficult: LLMs o…
PreScam: A Benchmark for Predicting Scam Progression from Early Conversations
Weixiang Sun, Shang Ma, Yiyang Li +5
Conversational scams, such as romance and investment scams, are emerging as a major form of online fraud. Unlike one-shot scam lures such as fake lottery or unpaid toll messages, t…
From UI to Code: Mobile Ads Detection via LLM-Unified Static-Dynamic Analysis
Shang Ma, Wei Cheng, Yanfang Ye +1
Mobile advertisements (ads) are essential to the app economy, yet detecting them is challenging because ad content is dynamically fetched from remote servers and rendered through d…
Agent+P: Guiding UI Agents via Symbolic Planning
Shang Ma, Xusheng Xiao, Yanfang Ye
Large Language Model (LLM)-based UI agents show great promise for UI automation but often hallucinate in long-horizon tasks due to their lack of understanding of the global UI tran…
PsyScam: A Benchmark for Psychological Techniques in Real-World Scams
Shang Ma, Tianyi Ma, Jiahao Liu +4
Over the years, online scams have grown dramatically, with nearly 50% of global consumers encountering scam attempts each week. These scams cause not only significant financial los…