Understanding and Analyzing Appraisal Systems in the Underground Marketplaces
Zhengyi Li, Xiaojing Liao
摘要
—An appraisal system is a feedback mechanism that has gained popularity in underground marketplaces. This system allows appraisers, who receive free samples from vendors, to provide assessments (i.e., appraisal reviews) for products in underground marketplaces. In this paper, we present the first measurement study on the appraisal system within underground marketplaces. Specifically, from 17M communication traces from eight marketplaces spanning from Feb 2006 to Mar 2023, we discover 56,229 appraisal reviews posted by 18,701 unique ap-praisers. We look into the appraisal review ecosystem, revealing five commonly used requirements and merits in the appraiser selection process. These findings indicate that the appraisal system is a well-established and structured process within the underground marketplace ecosystem. Furthermore, we reveal the presence of high-quality and unique cyber threat intelligence (CTI) in appraisal reviews. For example, we identify the ge-olocations of followers for a social booster and programming languages used for malware. Leveraging our extraction model, which integrates 41 distinct types of CTI, we capture 23,978 artifacts associated with 16,668 (50.2%) appraisal reviews. In contrast, artifacts are found in only 8.9% of listings and 2.7% of non-appraisal reviews. Our study provides valuable insights into this under-explored source of CTI, complementing existing research on threat intelligence gathering.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper12
- Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat IntelligenceXiaojing Liao, Kan Yuan, XiaoFeng Wang, Zhou Li 等CCS 2016 · 被引用 308 次
- FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security LiteratureZiyun Zhu, Tudor DumitrasCCS 2016 · 被引用 114 次
- Plug and Prey? Measuring the Commoditization of Cybercrime via Online Anonymous MarketsRolf van Wegberg, Samaneh Tajalizadehkhoob, Kyle Soska, Ugur Akyazi 等USENIX Security 2018 · 被引用 97 次
- Detecting Fake Accounts in Online Social Networks at the Time of RegistrationsDong Yuan, Yuanli Miao, Neil Zhenqiang Gong, Zheng Yang 等CCS 2019 · 被引用 86 次
- Reading Thieves' Cant: Automatically Identifying and Understanding Dark Jargons from Cybercrime MarketplacesKan Yuan, Haoran Lu, Xiaojing Liao, XiaoFeng WangUSENIX Security 2018 · 被引用 56 次
相关 Paper
- Evil Under the Sun: Understanding and Discovering Attacks on Ethereum Decentralized ApplicationsLiya Su, Xinyue Shen, Xiangyu Du, Xiaojing Liao 等USENIX Security 2021 · 被引用 74 次
- Malla: Demystifying Real-world Large Language Model Integrated Malicious ServicesZilong Lin, Jian Cui, Xiaojing Liao, XiaoFeng WangUSENIX Security 2024 · 被引用 49 次
- Enabling Efficient Cyber Threat Hunting With Cyber Threat IntelligencePeng Gao, Fei Shao, Xiaoyuan Liu, Xusheng Xiao 等ICDE 2021 · 被引用 124 次
- Actively Understanding the Dynamics and Risks of the Threat Intelligence EcosystemTillson Galloway, Omar Alrawi, Allen Chang, Athanasios Avgetidis 等NDSS 2026 · 被引用 2 次
- Into the Deep Web: Understanding E-commerce Fraud from Autonomous Chat with CybercriminalsPeng Wang, Xiaojing Liao, Yue Qin, XiaoFeng WangNDSS 2020
