Evasive Bots Masquerading as Human Beings on the Web

43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DNS 2013), June 2013, Budapest Hungary

Jing Jin, Jeff Offutt, Nan Zheng, Feng Mao, Aaron Koehl, and Haining Wang.

Abstract

Web bots such as crawlers are widely used to automate various online tasks over the Internet. In addition to the conventional approach of human interactive proofs such as CAPTCHAs, a more recent approach of human observational proofs (HOP) has been developed to automatically distinguish web bots from human users. Its design rationale is that web bots behave intrinsically differently from human beings, allowing them to be detected. This paper escalates the battle against web bots by exploring the limits of current HOP-based bot detection systems. We develop an evasive web bot system based on human behavioral patterns. Then we prototype a general web bot framework and a set of flexible de-classifier plugins, primarily based on application-level event evasion. We further abstract and define a set of benchmarks for measuring our system's evasion performance on contemporary web applications, including social network sites.Our results show that the proposed evasive system can effectively mimic human behaviorsand evade detectors by achieving high similarities between human users and evasive bots.This research provides a systematic way to explore the limitations of HOP-based web bot detectors.



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