OpenStreetMap中基于注意力的恶意破坏检测
Attention-Based Vandalism Detection in OpenStreetMap
摘要：
OpenStreetMap（OSM）是一个协作的众包Web地图，是一个开放的全球地图数据的独特来源，越来越多地被Web应用程序所采用。恶意破坏检测是支持信任和维护OSM透明性的关键任务。由于数据集的规模很大，贡献者的数量庞大，各种破坏形式以及缺乏注释数据，因此这项任务非常具有挑战性。本文介绍了Ovid -一种新的基于注意力的方法在OSM的破坏检测。Ovid依赖于一种新颖的神经架构，该架构采用多头注意力机制来有效地从OSM变更集中总结指示破坏行为的信息。为了便于自动破坏检测，我们引入了一组原始功能，捕获更改，用户和编辑信息。此外，我们首次从OSM编辑历史中提取了真实世界的破坏事件数据集，并将此数据集作为开放数据提供。我们对真实世界的故意破坏数据进行的评估证明了Ovid的有效性。
英文摘要：
OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
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。
