Crawler Detection in Location-Based Services Using Attributed Action Net

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.