System and method for detecting classes of automated browser agents
First Claim
1. A method for detecting automated browser agents, comprising:
- inserting a means for detecting information into a page code before a page is sent to a user'"'"'s browser, sending said page to a user'"'"'s browser, wherein said means sends emissions from one or more plugins via one or more channels, said emissions capturing client execution environment data without requiring a browser interaction and causing immediate and continued data collection of said client execution environment data, and transmitting via asynchronous HTTP posts said client execution environment data to an analysis server, wherein said analysis server compares said client execution environment data with a first database storing pattern characteristics for humans, a second database storing pattern characteristics for automated browser agents, and a third database storing pattern characteristics which are unclear as to whether performed by a human or a bot, thus forming a report on automated browser agent activity based on a qualitative evaluation of performance metrics collected,and making a conclusion on a probability of the user being an automated browser agent (a bot), said probability being based on which of said three databases has the highest match to said data.
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Abstract
A method for determining if a web browser is being operated by a human or a non-human agent, based on analysis of certain aspects of how a user interacts with a webpage. By placing a code snippet into the code of a webpage prior to a given user accessing that webpage, one is able to evaluate the user'"'"'s actions in order to predict the type of user. The predictions are made by acquiring information on how the user loads, navigates, and interacts with the webpage and comparing that information with statistics taken from a control group. Performance metrics from all webpages containing similar code elements are compiled by analysis servers and made available to the operator of a webpage through a variety of reporting mediums. By compiling such performance metrics, the method helps combat and prevent malicious automated traffic directed at advertisements and other aspects of a given webpage.
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Citations
20 Claims
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1. A method for detecting automated browser agents, comprising:
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inserting a means for detecting information into a page code before a page is sent to a user'"'"'s browser, sending said page to a user'"'"'s browser, wherein said means sends emissions from one or more plugins via one or more channels, said emissions capturing client execution environment data without requiring a browser interaction and causing immediate and continued data collection of said client execution environment data, and transmitting via asynchronous HTTP posts said client execution environment data to an analysis server, wherein said analysis server compares said client execution environment data with a first database storing pattern characteristics for humans, a second database storing pattern characteristics for automated browser agents, and a third database storing pattern characteristics which are unclear as to whether performed by a human or a bot, thus forming a report on automated browser agent activity based on a qualitative evaluation of performance metrics collected, and making a conclusion on a probability of the user being an automated browser agent (a bot), said probability being based on which of said three databases has the highest match to said data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A non-transitory computer readable medium storing a program causing a computer to execute a process, comprising:
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a first stage of identification, comprising grouping browsing activity based on origin, a second stage of data collection, comprising sending a page containing a pre-inserted code snippet for recording of particular client execution environment data, at page load and after page load, and transmitting said client execution environment data to an analysis server, a third stage of evaluation within said analysis server, comprising comparing said client execution environment data against control groups comprising a first database storing pattern characteristics for humans, a second database storing pattern characteristics for automated browser agents, and a third database storing pattern characteristics which are unclear as to whether performed by a human or a bot, and a fourth stage of reporting, comprising compiling a predictive report on bot activity based on a qualitative evaluation of performance metrics collected, said predictive report disclosing the highest matching database of said three databases. - View Dependent Claims (19, 20)
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Specification