SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis
Pranav Maneriker, Yuntian He, Srinivasan Parthasarathy
Abstract
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 further increasing the difficulty of linking users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and model interactions 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.
BibTeX
@inproceedings{maneriker-etal-2021-sysml,
title = "{SYSML}: {S}t{Y}lometry with {S}tructure and {M}ultitask {L}earning: {I}mplications for {D}arknet Forum Migrant Analysis",
author = "Maneriker, Pranav and
He, Yuntian and
Parthasarathy, Srinivasan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.548/",
doi = "10.18653/v1/2021.emnlp-main.548",
pages = "6844--6857"
}