Apparatus, method and article to facilitate automatic detection and removal of fraudulent user information in a network environment
A fraud detection system may obtain a number of known fraudulent end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes, attribute values, or both, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile. The fraud detection system can run these queries against incoming registrations to identify and screen fraudulent end-user profiles from entering the system and can also run these queries against stored end-user profile databases to identify and remove fraudulent or otherwise undesirable end-user profiles from the end-user database.
1. A method of operation in at least a portion of a system to detect suspected fraudulently generated profiles, the system which includes at least one processor and at least one nontransitory processor-readable medium that stores at least one of processor-executable instructions or processor-executable data, the at least one nontransitory processor-readable medium communicatively coupled to the at least one processor, the method of operation comprising:
computing, by the at least one processor, a value indicative of an initial probability of a profile in a set of profiles being at least one of a fraudulent profile [p(S)] or a valid profile [p(V)] based at least in part on historical profile data;
computing, by the at least one processor, for an attribute or a combination of attributes associated with the profiles in the set of profiles, a respective value indicative of a likelihood of the attribute or the combination of attributes being associated with at least one of a fraudulent profile [p(x i |S)] or a valid profile [p(x i |V)]; and
for each profile in the set of profiles, computing, by the at least one processor, a value indicative of a conditional probability that the respective profile is one of either fraudulent or valid based on an attribute or a combination of attributes associated with the respective profile determined at least in part using the respective initial probabilities and likelihoods.
2. The method of claim 1 wherein computing a value indicative of an initial probability of profiles being at least one of a fraudulent profile [p(S)] or a valid profile [p(V)] includes computing both a value indicative of the initial probability of profiles being fraudulent [p(S)] and a value indicative of the initial probability of profiles being valid [p(V)].
3. The method of claim 1 wherein computing a value indicative of a conditional probability that the respective profile is fraudulent includes computing the value indicative of a conditional probability that the respective profile is fraudulent according to:
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4. The method of claim 1 wherein computing a value indicative of a likelihood of the attribute or the combination of attributes being associated with at least one of a fraudulent profile [p(x i |S)] or a valid profile [p(x i |V)] includes computing the respective value indicative of a respective likelihood based at least in part on:
a Hyper Text Transfer Protocol (http) referrer associated with the respective profile;
an Internet Service Provider (ISP) associated with the respective profile;
an Internet Protocol (IP) of signup and last logins associated with the respective profile;
an identity of one or more plugins present in a browser associated with the respective profile;
a location (state/province/location) associated with a sign up for the respective profile;
an electronic mail (email) address associated with the respective profile;
an http via associated with the respective profile;
a most recent operating system (OS) associated with the respective profile;
one or more cookies used to track individual computers associated with the respective profile;
a username associated with the respective profile;
a user age associated with the respective profile;
one or more cookies that contain a user identifier of the most recent users to log in on using a given instance of a processor-based device;
a self-identified country associated with the respective profile;
an IP Country associated with the respective profile;
an identity of a browser used at signup associated with the respective profile;
a self-identified profession associated with the respective profile;
IP blocks of signup and at least two most recent logins associated with the respective profile;
an IP country of at least two most recent visits associated with the respective profile;
a time to create the respective profile;
an electronic mail (email) domain associated with the respective profile;
a self-identified postal or location code associated with the respective profile; or
one or more user keystrokes and cut and paste properties used in creating the respective profile.
5. The method of claim 1 , further comprising:
ranking the profiles into groups.
6. The method of claim 1 , further comprising:
providing result to a front end system for deletion.
7. A fraudulent profile detection system to detect at least one of accounts or related profiles suspected of being fraudulently generated, the system comprising:
at least one processor; and
at least one nontransitory processor-readable medium that stores at least one of processor-executable instructions or processor-executable data, the at least one nontransitory processor-readable medium communicatively coupled to the at least one processor, wherein the at least one processor:
computes a value indicative of an initial probability of a profile in a set of profiles being at least one of a fraudulent profile [p(S)] or a valid profile [p(V)] based at least in part on historical profile data;
for an attribute or a combination of attributes associated with the profiles in the set of profiles, computes a respective value indicative of a likelihood of the attribute or the combination of attributes being associated with at least one of a fraudulent profile [p(x i |S)] or a valid profile [p(x i |V)]; and
for each profile in the set of profiles, computes a value indicative of a conditional probability that the respective profile is one of either fraudulent or valid based on an attribute or a combination of attributes associated with the respective profile determined at least in part using the respective initial probabilities and likelihoods.
8. The system of claim 7 wherein the at least one processor computes both a value indicative of the initial probability of profiles being fraudulent [p(S)] and a value indicative of the initial probability of profiles being valid [p(V)].
9. The system of claim 7 wherein the at least one processor computes a value indicative of a conditional probability that the respective profile is fraudulent includes computing the value indicative of a conditional probability that the respective profile is fraudulent according to:
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10. The system of claim 7 wherein the at least one processor further:
ranks the profiles into groups using the respective value indicative of a conditional probability that the respective profile is fraudulent.
11. The system of claim 7 wherein the at least one processor further:
generates at least one output logically associated with each profile, the at least one output indicative of at least one of the following: a deletion indicator, a clearance indicator, or a further investigation indicator.