SYSML：具有结构和多任务学习的StYlometry：对暗网论坛移民分析的影响
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
摘要：
暗网市场论坛经常被用来在使用加密来隐藏身份的各方之间交换非法商品和服务。Tor网络用于托管这些市场，这保证了IP和位置跟踪的额外匿名化，使得使用多个帐户（sybils）链接恶意用户变得具有挑战性。此外，当一个论坛关闭时，用户会迁移到新的论坛，这使得很难跨多个论坛链接用户。我们开发了一种新的基于文体学的多任务学习方法，用于自然语言和交互建模，使用图嵌入构建低维表示的短情节的用户活动的作者归属。我们在四个不同的暗网论坛上对我们的方法进行了全面的评估，展示了其在最先进水平上的有效性，平均检索排名提升了2.5倍，Recall@10提升了2倍。
英文摘要：
Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed, making it difficult to link users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
