Hi Nirmal, As tharindu mentioned i would be planning to gather user identity data through the AbstractEventListener then those data will be used to train my model
Thanks, Asantha On Mon, Jun 27, 2016 at 9:26 AM, Tharindu Edirisinghe <[email protected]> wrote: > Hi Nirmal, > > The data is not in hand right now.. but we can assume we have data. > > Currently in Identity Server, it does not publish security related events > such as login, logout, password change etc. > > What Asantha would do is write a Listener ( i.e extend > AbstractUserOperationEventListener) and for each user operation it would > publish an event. > > Once he does it, we can perform some user operations with a load test and > gather data (get the events generated). > > Based on this data he needs to carry out the analysis. > > Thanks, > TharinduE > > On Mon, Jun 27, 2016 at 6:22 PM, Nirmal Fernando <[email protected]> wrote: > >> Seshika implemented a fraud detection toolbox using Markov chain models >> (using real-time siddhi queries) [1]. API-M Analytics product uses Markov >> chain model to detect abnormal resource access patterns. >> >> In WSO2 ML, we have implemented a clustering based anomaly detection >> algorithm [2]. >> >> [1] >> http://wso2.com/analytics/solutions/fraud-and-anomaly-detection-solution/ >> [2] >> https://docs.wso2.com/display/ML110/Generating+a+Model+Using+the+K+Means+Anomaly+Detection+Algorithm+with+Labeled+Data >> >> >> On Mon, Jun 27, 2016 at 5:45 PM, Tharindu Edirisinghe <[email protected] >> > wrote: >> >>> +DamithN, Seshika, Nirmal >>> >>> >>> @DamithN - I found the mail thread [1] bit similar. Are there any other >>> reference to the work you've done for that ? >>> >>> @Seshika, Nirmal - Do you guys have any input for the work Asantha is >>> trying to do ? He is a GSoC student that I mentor this year. >>> >>> Appreciate if you can help him out with this. >>> >>> >>> [1] "[Architecture] Security Authentication Analytics" >>> >>> Thanks, >>> TharinduE >>> >>> On Mon, Jun 27, 2016 at 4:15 PM, Asantha Thilina < >>> [email protected]> wrote: >>> >>>> Hi all, >>>> >>>> i am a GSOC student who doing the *project 21 : NoSQL User Store >>>> Development for Identity Server* and i am developing a *convolutional >>>> neural network* to detect *frauds* using deeplearning4j[1] for my >>>> research, i have done some case studies regarding fraud patterns ,i have >>>> mainly focused on frauds that can be occur in online money transactions and >>>> in login authentications i have refer some research papers[2],[3] and a >>>> white paper[4] regrading those possible fraud patterns >>>> I choosed convolutional neural network to develop my model but i am >>>> little confused about how could include those fraud patterns in to my model >>>> in a way it can detect a fraud in real time >>>> >>> >> Once you have a built model, you could write a siddhi extension to >> perform predictions (i.e. detect frauds in this case). >> >> Question: do we have data in hand? or are you trying to build a model on >> the fly? >> >> and also is convolutional network is a best way to achieve my task or is >>>> there any better method than this?,i would be grateful if anyone can guide >>>> me to achieve this task >>>> [1]http://deeplearning4j.org/convolutionalnets >>>> [2] >>>> http://www.ijsce.org/attachments/File/NCAI2011/IJSCE_NCAI2011_025.pdf >>>> [3] >>>> https://www.researchgate.net/publication/200795976_Fraud_Detection_using_Neural_Networks >>>> [4]https://neo4j.com/resources/fraud-detection-white-paper/ >>>> >>>> Thanks, >>>> Asantha >>>> >>> >>> >>> >>> -- >>> >>> Tharindu Edirisinghe >>> Senior Software Engineer | WSO2 Inc >>> Platform Security Team >>> Blog : tharindue.blogspot.com >>> mobile : +94 775181586 >>> >> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Team Lead - WSO2 Machine Learner >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > > Tharindu Edirisinghe > Senior Software Engineer | WSO2 Inc > Platform Security Team > Blog : tharindue.blogspot.com > mobile : +94 775181586 >
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