Waruna, we do not know a data set yet, if there is one we would love to use it.
One thought is taking KDD network activity dataset ( which is well known benchmark for anomaly detection) and create a new dataset using that. --Srinath On Tue, Nov 18, 2014 at 1:48 PM, Seshika Fernando <[email protected]> wrote: > Well the first usecase we are aiming for is Credit Card Fraud Detection at > Merchant level. We do not have readily available datasets (since these are > credit card transactions, usually this data is not readily available). > However, at this point we are only doing a POC to show that our system is > able to perform this kind of functions. It is only a matter of fine tuning > the weights assigned to the rules, which can be done when we implement this > for customers. > The weights are largely dependent on the merchant type/size etc; > > > > On Tue, Nov 18, 2014 at 1:37 PM, Waruna Perera <[email protected]> wrote: > >> Hi Seshika, >> >> Are you planning to test this based on some real world scenarios and >> data? ( ex: stock prices) Therefore to get and idea how accurate we can be. >> >> Thanks, >> >> On Tue, Nov 18, 2014 at 10:59 AM, Seshika Fernando <[email protected]> >> wrote: >> >>> Hi all, >>> >>> Following the implementation of Fraud Rules, and Markov Chain capability >>> in order to do outlier detection in CEP, we are hoping to implement Fraud >>> Scoring capability. >>> >>> Fraud Scoring is a mechanism to evaluate multiple features of a >>> transaction (eg:- geolocation, ip address, billing/shipping address, >>> transaction velocity etc;) and based on historical trends and blacklists, >>> compute a score for each transaction (eg:- between 0 and 100). Higher the >>> score, higher the risk that the transaction will be a fraudulent >>> transaction. [1] is a good introduction to Fraud Scoring. >>> >>> Now that we have already implemented several fraud detection rules, the >>> plan is to augment this using a scoring system, so that siddhi calculates a >>> score for each transaction, based on how it performed in the rules. [2] >>> gives a very simple example of how this might be done. >>> >>> When we complete this, we are able to show that CEP can perform fraud >>> detection in the following ways >>> a. Rule based >>> b. Using Markov Chains >>> c. Using Fraud Scores >>> >>> 1. http://www.fraudpractice.com/fl-fraudscore.html >>> 2. https://www.maxmind.com/en/ccfd_formula >>> >>> Cheers, >>> Seshika >>> >>> >>> >>> _______________________________________________ >>> Architecture mailing list >>> [email protected] >>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>> >>> >> >> >> -- >> Waruna Perera >> Senior Software Engineer - Test Automation >> Mobile: +94 77 3867037 >> WSO2, Inc.; http://wso2.com/ >> lean . enterprise . middlewear. >> >> _______________________________________________ >> Architecture mailing list >> [email protected] >> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >> >> > > _______________________________________________ > Architecture mailing list > [email protected] > https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture > > -- ============================ Srinath Perera, Ph.D. http://people.apache.org/~hemapani/ http://srinathsview.blogspot.com/
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