OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection.
arXiv: Social and Information Networks(2017)
摘要
When tasked to find fraudulent social network users, what is a practitioner to do? Traditional classification can lead to poor generalization and high misclassification given few and possibly biased labels. We tackle this problem by analyzing fraudulent behavioral patterns, featurizing users to yield strong discriminative performance, and building algorithms to handle new and multimodal fraud types. First, we set up honeypots, or dummy social network accounts on which we solicit fake followers (after careful IRB approval). We report the signs of such behaviors, including oddities in local network connectivity, account attributes, and similarities and differences across fraud providers. We discover several types of fraud behaviors, with the possibility of even more. We discuss how to leverage these insights in practice, build strongly performing entropy-based features, and propose OEC (Open-ended Classification), an approach for future-proofing existing algorithms to account for the complexities of link fraud. Our contributions are (a) observations: we analyze our honeypot fraudster ecosystem and give insights regarding various fraud behaviors, (b) features: we engineer features which give exceptionally strong (u003e0.95 precision/recall) discriminative power on ground-truth data, and (c) algorithm: we motivate and discuss OEC, which reduces misclassification rate by u003e18% over baselines and routes practitioner attention to samples at high-risk of misclassification.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络