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  • Fraud Detection with the SQL Server Suite Part 1

    - by Dejan Sarka
    While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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  • Interview with Tim Danaher - Editor of Retail Week

    - by sarah.taylor(at)oracle.com
    Last week I caught up with Tim Danaher from Retail Week about the judging process for the Oracle Retail Week Awards.  It was great to get Tim's perspective on the retail industry and his thoughts on emerging trends in the entries this year.   The Oracle Retail Week Awards are going to be very exciting this year and I'm very priviledged to be presenting awards to winners again.  The awards ceremony is on March 17th - if you're coming then I look forward to seeing you there. 

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  • Fraud Detection with the SQL Server Suite Part 2

    - by Dejan Sarka
    This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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  • Possible automated Bing Ads fraud?

    - by Gary Joynes
    I run a website that generates life insurance leads. The site is very simple a) there is a form for capturing the user's details, life insurance requirements etc b) A quote comparison feature We drive traffic to our site using conventional Google Adwords and Bing Ads campaigns. Since the 6th January we have received 30-40 dodgy leads which have the following in common: All created between 2 and 8 AM Phone number always in the format "123 1234 1234' Name, Date Of Birth, Policy details, Address all seem valid and are unique across the leads Email addresses from "disposable" email accounts including dodgit.com, mailinator.com, trashymail.com, pookmail.com Some leads come from the customer form, some via the quote comparison feature All come from different IP addresses We get the keyword information passed through from the URLs All look to be coming from Bing Ads All come from Internet Explorer v7 and v8 The consistency of the data and the random IP addresses seem to suggest an automated approach but I'm not sure of the intent. We can handle identifying these leads within our database but is there anyway of stopping this at the Ad level i.e. before the click through.

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  • Fraud and Anomaly Detection using Oracle Data Mining YouTube-like Video

    - by chberger
    I've created and recorded another YouTube-like presentation and "live" demos of Oracle Advanced Analytics Option, this time focusing on Fraud and Anomaly Detection using Oracle Data Mining.  [Note:  It is a large MP4 file that will open and play in place.  The sound quality is weak so you may need to turn up the volume.] Data is your most valuable asset. It represents the entire history of your organization and its interactions with your customers.  Predictive analytics leverages data to discover patterns, relationships and to help you even make informed predictions.   Oracle Data Mining (ODM) automatically discovers relationships hidden in data.  Predictive models and insights discovered with ODM address business problems such as:  predicting customer behavior, detecting fraud, analyzing market baskets, profiling and loyalty.  Oracle Data Mining, part of the Oracle Advanced Analytics (OAA) Option to the Oracle Database EE, embeds 12 high performance data mining algorithms in the SQL kernel of the Oracle Database. This eliminates data movement, delivers scalability and maintains security.  But, how do you find these very important needles or possibly fraudulent transactions and huge haystacks of data? Oracle Data Mining’s 1 Class Support Vector Machine algorithm is specifically designed to identify rare or anomalous records.  Oracle Data Mining's 1-Class SVM anomaly detection algorithm trains on what it believes to be considered “normal” records, build a descriptive and predictive model which can then be used to flags records that, on a multi-dimensional basis, appear to not fit in--or be different.  Combined with clustering techniques to sort transactions into more homogeneous sub-populations for more focused anomaly detection analysis and Oracle Business Intelligence, Enterprise Applications and/or real-time environments to "deploy" fraud detection, Oracle Data Mining delivers a powerful advanced analytical platform for solving important problems.  With OAA/ODM you can find suspicious expense report submissions, flag non-compliant tax submissions, fight fraud in healthcare claims and save huge amounts of money in fraudulent claims  and abuse.   This presentation and several brief demos will show Oracle Data Mining's fraud and anomaly detection capabilities.  

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  • Private domain purchase with paypal: how to prevent fraud?

    - by whamsicore
    I am finally going to buy a domain I have been looking at. The domain owner wants me to give him my Godaddy account information and send him the payment via Paypal gift, so that there will be no extra charges. Should this cause suspicion? Does Paypal offer any kind of fraud protection? What is the best way to protect myself from fraud in this situation, without the need for escrow services, such as escrow.com? Any advice welcomed. Thanks.

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  • AJI Report #12 | Tim Hibbard Talks .NET to iOS Development

    - by Jeff Julian
    In this AJI Report, Jeff and John talk with Tim Hibbard of Engraph Software about making the transition from a .NET developer to mobile applications using the iOS platform. Tim dives into what each experience was like from getting into XCode for the first time, using Third-party tools, Apple's design guidelines, and provisioning an app to the App Store. Tim has been a .NET developer since the framework was released in 2001 and now has two mobile applications in production. Listen to the Show Site: http://engraph.com/ Blog: http://timhibbard.com/blog/ Twitter: @timhibbard

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  • Welcome to Linchpin People, Tim Mitchell!

    - by andyleonard
    I am honored to welcome Tim Mitchell ( blog | @Tim_Mitchell ) to Linchpin People! Tim brings years of experience consulting  with SQL Server, Integration Services, and Business Intelligence to our growing organization. I am overjoyed to be able to work with my friend! Rather than babble on about Linchpin People (using words like "synergy" and "world class"), I direct you to Tim's awesome remarks on his transition , and end with a simple "w00t!" :{>...(read more)

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  • GDD-BR 2010 [1D] Tim Bray - Android Ecosystem and What's New

    GDD-BR 2010 [1D] Tim Bray - Android Ecosystem and What's New Speaker: Tim Bray Track: Android Time slot: D[13:50 - 14:35] Room: 1 Level: 101 This talk combines an introduction to the Android ecosystem with a description of what's new in it. The ecosystem includes the technology, developer community, Android Market, and of course the huge population of Android users. From: GoogleDevelopers Views: 25 1 ratings Time: 41:40 More in Science & Technology

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  • LexisNexis and Oracle Join Forces to Prevent Fraud and Identity Abuse

    - by Tanu Sood
    Author: Mark Karlstrand About the Writer:Mark Karlstrand is a Senior Product Manager at Oracle focused on innovative security for enterprise web and mobile applications. Over the last sixteen years Mark has served as director in a number of tech startups before joining Oracle in 2007. Working with a team of talented architects and engineers Mark developed Oracle Adaptive Access Manager, a best of breed access security solution.The world’s top enterprise software company and the world leader in data driven solutions have teamed up to provide a new integrated security solution to prevent fraud and misuse of identities. LexisNexis Risk Solutions, a Gold level member of Oracle PartnerNetwork (OPN), today announced it has achieved Oracle Validated Integration of its Instant Authenticate product with Oracle Identity Management.Oracle provides the most complete Identity and Access Management platform. The only identity management provider to offer advanced capabilities including device fingerprinting, location intelligence, real-time risk analysis, context-aware authentication and authorization makes the Oracle offering unique in the industry. LexisNexis Risk Solutions provides the industry leading Instant Authenticate dynamic knowledge based authentication (KBA) service which offers customers a secure and cost effective means to authenticate new user or prove authentication for password resets, lockouts and such scenarios. Oracle and LexisNexis now offer an integrated solution that combines the power of the most advanced identity management platform and superior data driven user authentication to stop identity fraud in its tracks and, in turn, offer significant operational cost savings. The solution offers the ability to challenge users with dynamic knowledge based authentication based on the risk of an access request or transaction thereby offering an additional level to other authentication methods such as static challenge questions or one-time password when needed. For example, with Oracle Identity Management self-service, the forgotten password reset workflow utilizes advanced capabilities including device fingerprinting, location intelligence, risk analysis and one-time password (OTP) via short message service (SMS) to secure this sensitive flow. Even when a user has lost or misplaced his/her mobile phone and, therefore, cannot receive the SMS, the new integrated solution eliminates the need to contact the help desk. The Oracle Identity Management platform dynamically switches to use the LexisNexis Instant Authenticate service for authentication if the user is not able to authenticate via OTP. The advanced Oracle and LexisNexis integrated solution, thus, both improves user experience and saves money by avoiding unnecessary help desk calls. Oracle Identity and Access Management secures applications, Juniper SSL VPN and other web resources with a thoroughly modern layered and context-aware platform. Users don't gain access just because they happen to have a valid username and password. An enterprise utilizing the Oracle solution has the ability to predicate access based on the specific context of the current situation. The device, location, temporal data, and any number of other attributes are evaluated in real-time to determine the specific risk at that moment. If the risk is elevated a user can be challenged for additional authentication, refused access or allowed access with limited privileges. The LexisNexis Instant Authenticate dynamic KBA service plugs into the Oracle platform to provide an additional layer of security by validating a user's identity in high risk access or transactions. The large and varied pool of data the LexisNexis solution utilizes to quiz a user makes this challenge mechanism even more robust. This strong combination of Oracle and LexisNexis user authentication capabilities greatly mitigates the risk of exposing sensitive applications and services on the Internet which helps an enterprise grow their business with confidence.Resources:Press release: LexisNexis® Achieves Oracle Validated Integration with Oracle Identity Management Oracle Access Management (HTML)Oracle Adaptive Access Manager (pdf)

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  • Silverlight TV 22: Tim Heuer on Extending the SMF

    In this episode of Silverlight TV, Tim Heuer demonstrates how to use the Silverlight Media Framework (SMF) to create a nice media experience akin to what has been demonstrated through the 2010 Winter Olympics and Sunday Night Football players. He also demonstrates how to encode smooth streaming video using Expression Encoder and play the video using SMF. Tim also shows how he extended the SMF implementation and created a player to suit his specific needs (including not using DVR, among other features)....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Silverlight TV 16: Tim Heuer and Jesse Liberty Talk about Silverlight 4 RC at MIX 10

      While at MIX10, John catches up with Jesse Liberty and Tim Heuer to discuss their favorite features in Silverlight 4 on this episode of Silverlight TV. Along with calling out and discussing why they're each impressed with their favorite features, Jesse, Tim, and John also discuss the impact of the announcements made at MIX regarding development for WP7 and Silverlight at the Day 1 keynote. You can also check out the 60+ page whitepaper that covers the full feature list of all the new features...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Dernière minute : Le co-créateur du XML quitte Oracle pour Google, Tim Bray travaillera sur Android

    Dernière minute : Le co-créateur du XML quitte Oracle pour Google, Tim Bray travaillera sur Android Tim Bray, qui avait été à l'origine (en partie) de l'écriture du XML, vient d'annoncer il y a quelques heures sur son blog qu'il quittait Sun/Oracle pour rejoindre Google à un poste d'"Advocate Developper" centré sur Android. Il prédit que cela sera très excitant et semble ravi de ce changement. Il faut dire que ces derniers mois, ses relations avec son précédent employeur s'étaient quelque peu détériorées, notamment lorsqu'on lui avait fait censurer son blog à propos de la fusion Sun/Oracle. Il a donc cherché à quitter ses fonctions et a trouvé refuge chez Google, qui a carrément crée un poste juste p...

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  • Prevent Click Fraud in Advertisement system with PHP and Javascript

    - by CodeDevelopr
    I would like to build an Advertising project with PHP, MySQL, and Javascript. I am talking about something like... Google Adsense BuySellAds.com Any other advertising platform My question is mainly, what do I need to look out for to prevent people cheating the system and any other issues I may encounter? My design concept. An Advertisement is a record in the Database, when a page is loaded, using Javascript, it calls my server which in turn will use a PHP script to query the Database and get a random Advertisement. (It may do kore like get an ad based on demographics or other criteria as well) The PHP script will then return the Advertisement to the server/website that is calling it and show it on the page as an Image that will have a special tracking link. I will need to... Count all impressions (when the Advertisement is shown on the page) Count all clicks on the Advertisement link Count all Unique clicks on the Advertisement link My question is purely on the query and displaying of the Advertisement and nothing to do with the administration side. If there is ever money involved with my Advertisement buying/selling of adspace, then the stats need to be accurate and make sure people can't easily cheat the system. Is tracking IP address really the only way to try to prevent click fraud? I am hoping someone with some experience can clarify I am on the right track? As well as give me any advice, tips, or anything else I should know about doing something like this?

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  • SQLPeople Interviews - Crys Manson, Jeremiah Peschka, and Tim Mitchell

    - by andyleonard
    Introduction Late last year I announced an exciting new endeavor called SQLPeople . At the end of 2010 I announced the 2010 SQLPeople Person of the Year . Check out these interviews from your favorite SQLPeople ! Interviews To Date Tim Mitchell Jeremiah Peschka Crys Manson Ben McEwan Thomas LaRock Lori Edwards Brent Ozar Michael Coles Rob Farley Jamie Thomson Conclusion I plan to post two or three interviews each week for the forseeable future. SQLPeople is just one of the cool new things I get to...(read more)

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  • Tim Heuer: A Guide to What Has Changed in Silverlight 4 RC

    Understand what has changed in the release candidate since the beta. The features still exist, but there are some changes to the implementations of some of the features, as well as some new ones....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Android vs. iPhone: Google Hires Tim Bray

    <b>Linux Planet:</b> ""The iPhone vision of the mobile Internet&#8217;s future omits controversy, sex, and freedom, but includes strict limits on who can know what and who can say what," he wrote. "It's a sterile, Disney-fied walled garden surrounded by sharp-toothed lawyers.""

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  • Krita Gemini, 2 fois plus agréable sur un 2 en 1, par Tim Duncan

    Bonjour,Je vous présente cet article intitulé : "Krita Gemini, 2 fois plus agréable sur un 2 en 1" Au fil des années, les ordinateurs ont utilisé une variété de méthodes d'entrée à partir des cartes perforées en passant par des lignes de commande jusqu'à pointer-et-cliquer avec une souris ou d'autres périphériques. Avec l'adoption des écrans tactiles, nous pouvons maintenant pointer-et-cliquer avec une souris, un stylet ou avec les doigts. La plupart d'entre nous ne sommes pas encore...

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  • Spring security request matcher is not working with regex

    - by Felipe Cardoso Martins
    Using Spring MVC + Security I have a business requirement that the users from SEC (Security team) has full access to the application and FRAUD (Anti-fraud team) has only access to the pages that URL not contains the words "block" or "update" with case insensitive. Bellow, all spring dependencies: $ mvn dependency:tree | grep spring [INFO] +- org.springframework:spring-webmvc:jar:3.1.2.RELEASE:compile [INFO] | +- org.springframework:spring-asm:jar:3.1.2.RELEASE:compile [INFO] | +- org.springframework:spring-beans:jar:3.1.2.RELEASE:compile [INFO] | +- org.springframework:spring-context:jar:3.1.2.RELEASE:compile [INFO] | +- org.springframework:spring-context-support:jar:3.1.2.RELEASE:compile [INFO] | \- org.springframework:spring-expression:jar:3.1.2.RELEASE:compile [INFO] +- org.springframework:spring-core:jar:3.1.2.RELEASE:compile [INFO] +- org.springframework:spring-web:jar:3.1.2.RELEASE:compile [INFO] +- org.springframework.security:spring-security-core:jar:3.1.2.RELEASE:compile [INFO] | \- org.springframework:spring-aop:jar:3.0.7.RELEASE:compile [INFO] +- org.springframework.security:spring-security-web:jar:3.1.2.RELEASE:compile [INFO] | +- org.springframework:spring-jdbc:jar:3.0.7.RELEASE:compile [INFO] | \- org.springframework:spring-tx:jar:3.0.7.RELEASE:compile [INFO] +- org.springframework.security:spring-security-config:jar:3.1.2.RELEASE:compile [INFO] +- org.springframework.security:spring-security-acl:jar:3.1.2.RELEASE:compile Bellow, some examples of mapped URL path from spring log: Mapped URL path [/index] onto handler 'homeController' Mapped URL path [/index.*] onto handler 'homeController' Mapped URL path [/index/] onto handler 'homeController' Mapped URL path [/cellphone/block] onto handler 'cellphoneController' Mapped URL path [/cellphone/block.*] onto handler 'cellphoneController' Mapped URL path [/cellphone/block/] onto handler 'cellphoneController' Mapped URL path [/cellphone/confirmBlock] onto handler 'cellphoneController' Mapped URL path [/cellphone/confirmBlock.*] onto handler 'cellphoneController' Mapped URL path [/cellphone/confirmBlock/] onto handler 'cellphoneController' Mapped URL path [/user/update] onto handler 'userController' Mapped URL path [/user/update.*] onto handler 'userController' Mapped URL path [/user/update/] onto handler 'userController' Mapped URL path [/user/index] onto handler 'userController' Mapped URL path [/user/index.*] onto handler 'userController' Mapped URL path [/user/index/] onto handler 'userController' Mapped URL path [/search] onto handler 'searchController' Mapped URL path [/search.*] onto handler 'searchController' Mapped URL path [/search/] onto handler 'searchController' Mapped URL path [/doSearch] onto handler 'searchController' Mapped URL path [/doSearch.*] onto handler 'searchController' Mapped URL path [/doSearch/] onto handler 'searchController' Bellow, a test of the regular expressions used in spring-security.xml (I'm not a regex speciality, improvements are welcome =]): import java.util.Arrays; import java.util.List; public class RegexTest { public static void main(String[] args) { List<String> pathSamples = Arrays.asList( "/index", "/index.*", "/index/", "/cellphone/block", "/cellphone/block.*", "/cellphone/block/", "/cellphone/confirmBlock", "/cellphone/confirmBlock.*", "/cellphone/confirmBlock/", "/user/update", "/user/update.*", "/user/update/", "/user/index", "/user/index.*", "/user/index/", "/search", "/search.*", "/search/", "/doSearch", "/doSearch.*", "/doSearch/"); for (String pathSample : pathSamples) { System.out.println("Path sample: " + pathSample + " - SEC: " + pathSample.matches("^.*$") + " | FRAUD: " + pathSample.matches("^(?!.*(?i)(block|update)).*$")); } } } Bellow, the console result of Java class above: Path sample: /index - SEC: true | FRAUD: true Path sample: /index.* - SEC: true | FRAUD: true Path sample: /index/ - SEC: true | FRAUD: true Path sample: /cellphone/block - SEC: true | FRAUD: false Path sample: /cellphone/block.* - SEC: true | FRAUD: false Path sample: /cellphone/block/ - SEC: true | FRAUD: false Path sample: /cellphone/confirmBlock - SEC: true | FRAUD: false Path sample: /cellphone/confirmBlock.* - SEC: true | FRAUD: false Path sample: /cellphone/confirmBlock/ - SEC: true | FRAUD: false Path sample: /user/update - SEC: true | FRAUD: false Path sample: /user/update.* - SEC: true | FRAUD: false Path sample: /user/update/ - SEC: true | FRAUD: false Path sample: /user/index - SEC: true | FRAUD: true Path sample: /user/index.* - SEC: true | FRAUD: true Path sample: /user/index/ - SEC: true | FRAUD: true Path sample: /search - SEC: true | FRAUD: true Path sample: /search.* - SEC: true | FRAUD: true Path sample: /search/ - SEC: true | FRAUD: true Path sample: /doSearch - SEC: true | FRAUD: true Path sample: /doSearch.* - SEC: true | FRAUD: true Path sample: /doSearch/ - SEC: true | FRAUD: true Tests Scenario 1 Bellow, the important part of spring-security.xml: <security:http entry-point-ref="entryPoint" request-matcher="regex"> <security:intercept-url pattern="^.*$" access="ROLE_SEC" /> <security:intercept-url pattern="^(?!.*(?i)(block|update)).*$" access="ROLE_FRAUD" /> <security:access-denied-handler error-page="/access-denied.html" /> <security:form-login always-use-default-target="false" login-processing-url="/doLogin.html" authentication-failure-handler-ref="authFailHandler" authentication-success-handler-ref="authSuccessHandler" /> <security:logout logout-url="/logout.html" success-handler-ref="logoutSuccessHandler" /> </security:http> Behaviour: FRAUD group **can't" access any page SEC group works fine Scenario 2 NOTE that I only changed the order of intercept-url in spring-security.xml bellow: <security:http entry-point-ref="entryPoint" request-matcher="regex"> <security:intercept-url pattern="^(?!.*(?i)(block|update)).*$" access="ROLE_FRAUD" /> <security:intercept-url pattern="^.*$" access="ROLE_SEC" /> <security:access-denied-handler error-page="/access-denied.html" /> <security:form-login always-use-default-target="false" login-processing-url="/doLogin.html" authentication-failure-handler-ref="authFailHandler" authentication-success-handler-ref="authSuccessHandler" /> <security:logout logout-url="/logout.html" success-handler-ref="logoutSuccessHandler" /> </security:http> Behaviour: SEC group **can't" access any page FRAUD group works fine Conclusion I did something wrong or spring-security have a bug. The problem already was solved in a very bad way, but I need to fix it quickly. Anyone knows some tricks to debug better it without open the frameworks code? Cheers, Felipe

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  • Is my webserver being abused for banking fraud?

    - by koffie
    Since a few weeks i'm getting a lot of 403 errors from apache in my log files that seem to be related to a bank frauding scheme. The relevant log entries look like this (The ip 1.2.3.4 is one I made up, I did not modify the rest of each line) www.bradesco.com.br:80 / 1.2.3.4 - - [01/Dec/2012:07:20:32 +0100] "GET / HTTP/1.1" 403 427 "-" "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11" www.bb.com.br:80 / 1.2.3.4 - - [01/Dec/2012:07:20:32 +0100] "GET / HTTP/1.1" 403 370 "-" "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11" www.santander.com.br:80 / 1.2.3.4 - - [01/Dec/2012:07:20:33 +0100] "GET / HTTP/1.1" 403 370 "-" "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11" www.banese.com.br:80 / 1.2.3.4 - - [01/Dec/2012:07:20:33 +0100] "GET / HTTP/1.1" 403 370 "-" "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11" the logformat I use is: LogFormat "%V:%p %U %h %l %u %t \"%r\" %>s %O \"%{Referer}i\" \"%{User-Agent}i\"" The strange thing is that all these domains are domains of banks and 3 out of the 4 domains are also in the list of the bank frauding scheme described on: http://www.abuse.ch/?p=2925 I would really like to know if my server is being abused for bank frauding or not. I suspect not, because it's giving 403 to all requests. But any extra checks that I can do to ensure that my server is not being abused are welcome. I'm also curious on how the "bad guys" expected my server to behave. I.e. are they just expecting my server to act as a proxy to hide the ip of the fake site, or are they expecting that my server will actually serve the fake banking website? Is the ip 1.2.3.4 more likely to be the ip of a victim or the ip of a bad guy. I suspect a bad guy, because it's quite unlikely that a real person would visit 4 bank sites in a second. If it's from a bad guy I'm very curious at what he is trying to do.

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