Crawler Detection in Location-Based Services Using Attributed Action Net

an hui wang

Abstract:

Malicious Web crawlers threaten information system due to heavily taking up bandwidth resources and stealing private user data.
Ele.me, a prevalent on-demand food delivery platform in China,
suffers from the negative impact of crawlers. The crawler detection
systems face two major challenges: spatial patterns of the crawler
behaviors and limited labeled data for training. In this paper, we
present efficient solutions to tackle these challenges. Specifically,
we propose a new Attributed Action Net (AANet for short) model
to detect Location-Based Services (LBS) crawlers and a three-stage
learning framework to train the model. AANet consists of three
different embedding modules, including the action token sequence,
temporal-spatial attributes of users, and the context information
of the raw data. We have deployed the model at Ele.me, and both
offline experiments and online A/B tests show that the proposed
method is superior to the state-of-the-art models for sequence data
classification on the food delivery platform.