SBotMiner: Large Scale Search Bot Detection

Fang Yu, Yinglian Xie, and Qifa Ke

Abstract

In this paper, we study search bot traffic from search engine query

logs at a large scale. Although bots that generate search traffic

aggressively can be easily detected, a large number of distributed,

low rate search bots are difficult to identify and are often associated

with malicious attacks. We present SBotMiner, a system for

automatically identifying stealthy, low-rate search bot traffic from

query logs. Instead of detecting individual bots, our approach captures

groups of distributed, coordinated search bots. Using sampled

data from two different months, SBotMiner identifies over 123

million bot-related pageviews, accounting for 3.8% of total traffic.

Our in-depth analysis shows that a large fraction of the identified

bot traffic may be associated with various malicious activities such

as phishing attacks or vulnerability exploits. This finding suggests

that detecting search bot traffic holds great promise to detect and

stop attacks early on.

Details

Publication typeInproceedings
Published inACM International Conference on Web Search and Data Mining (WSDM)
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