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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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  • 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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  • Spam prevention through IP tracking

    - by whamsicore
    I am building a website with user generated comments. In order to implement user moderation/spam-protection, users have the ability to mark comments as spam. When one comment is marked as spam, I want all comments from the same IP address to be deleted. I am not familiar with spam prevention in general, other than Captcha. Question: is this a feasible/good system for spam prevention? are there better ways, or improvements I can make? Thanks.

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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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  • Exchange 2010 email spoofing prevention

    - by holian
    Masters, Unfortunately we got some spam mail which seems to be coming from our own domain. I found some article which all says to remove Anonymous login from internet receive connector (http://exchangepedia.com/2008/09/how-to-prevent-annoying-spam-from-your-own-domain.html) I think i something misunderstood about those articles, because if i remove the Anonymous connection e-mails did not receive from external address (like gmail - Diagnostic-Code: SMTP; 530 5.7.1 Client was not authenticated) Some pictures about our configuration:

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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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  • 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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  • Database Security: The First Step in Pre-Emptive Data Leak Prevention

    - by roxana.bradescu
    With WikiLeaks raising awareness around information leaks and the harm they can cause, many organization are taking stock of their own information leak protection (ILP) strategies in 2011. A report by IDC on data leak prevention stated: Increasing database security is one of the most efficient and cost-effective measures an organization can take to prevent data leaks. By utilizing the data protection, access control, account management, encryption, log management, and other security controls inherent in the database management system, entities can institute first-level control over the widest range of protected information. As a central repository for unstructured data, which is growing at leaps and bounds, the database should be the first layer providing information leakage protection. Unfortunately, most organizations are not taking sufficient steps to protect their databases according to a survey of the Independent Oracle User Group. For example, any operating system administrator or database administrator can access the all the data stored in the database in most organizations. Without any kind of auditing or monitoring. And it's not just administrators, database users can typically access the database with ad-hoc query tools from their desktop and by-pass any application level controls. Despite numerous regulations calling for controls to limit the powers of insiders, most organizations still put too many privileges in the hands of their employees. Time and time again these excess privileges have backfired. Internal agents were implicated in almost half of data breaches according to the Verizon Data Breach Investigations Report and the rate is rising. Hackers also took advantage of these excess privileges very successfully using stolen credentials and SQL injection attacks. But back to the insiders. Who are these insiders and why do they do it? In 2002, the U.S. Secret Service (USSS) behavioral psychologists and CERT information security experts formed the Insider Threat Study team to examine insider threat cases that occurred in US critical infrastructure sectors, and examined them from both a technical and a behavioral perspective. A series of fascinating reports has been published as a result of this work. You can learn more by watching the ISSA Insider Threat Web Conference. So as your organization starts to look at data leak prevention over the coming year, start off by protecting your data at the source - your databases. IDC went on to say: Any enterprise looking to improve its competitiveness, regulatory compliance, and overall data security should consider Oracle's offerings, not only because of their database management capabilities but also because they provide tools that are the first layer of information leak prevention. Learn more about Oracle Database Security solutions and get the whitepapers, demos, tutorials, and more that you need to protect data privacy from internal and external threats.

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  • CSRF (Cross-site request forgery) attack example and prevention in PHP

    - by Saif Bechan
    I have an website where people can place a vote like this: http://mysite.com/vote/25 This will place a vote on item 25. I want to only make this available for registered users, and only if they want to do this. Now I know when someone is busy on the website, and someone gives them a link like this: http://mysite.com/vote/30 then the vote will be places for him on the item without him wanting to do this. I have read the explanation on the OWASP website, but i don't really understand it Is this an example of CSFR, and how can I prevent this. The best thing i can think off is adding something to the link like a hash. But this will be quite irritating to put something on the end of all the links. Is there no other way of doing this. Another thing can someone maybe give me some other example of this, because the website seems fairly fugue to me.

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  • Possible Data Execution Prevention problem in Windows 7

    - by Joel in Gö
    I have a serious problem with my .Net program. It calls a native dll, and then crashes instantly because it can't find a native method. This is behaviour we have seen before, whereby the C# compiler, in its infinite wisdom, sets the flag that the program is DEP compatible, even if it calls a native dll which patently is not. We have the standard workaround for this, where the flag is set to Not DEP Compatible in a post-build step, and this works fine. Everywhere except on my machine. I have Windows 7 32bit, and the program works fine on the Win 7 64bit machines that we have, as well as on Vista and XP; we have not yet been able to check on another Win7 32bit. However, on my machine the DataExecutionPolicy_SupportPolicy is 0, i.e. we have successfully switched DEP off. The dll in question also works fine when called from a native program. We are running out of ideas... any help would be much appreciated!

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  • Possible Data Execution Prevention (DEP) problem in Windows 7

    - by Joel in Gö
    I have a serious problem with my .Net program. It calls a native dll, and then crashes instantly because it can't find a native method. This is behaviour we have seen before, whereby the C# compiler, in its infinite wisdom, sets the flag that the program is DEP compatible, even if it calls a native dll which patently is not. We have the standard workaround for this, where the flag is set to Not DEP Compatible in a post-build step, and this works fine. Everywhere except on my machine. I have Windows 7 32bit, and the program works fine on the Win 7 64bit machines that we have, as well as on Vista and XP; we have not yet been able to check on another Win7 32bit. However, on my machine the DataExecutionPolicy_SupportPolicy is 0, i.e. we have successfully switched DEP off. Does anyone know whether there is some situation in which it can still act? Or any other mechanism which could have the same effect? The dll in question also works fine when called from a native program. We are running out of ideas... any help would be much appreciated!

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  • [Livre]:Chaînes d'exploits: Scénarios de hacking avancé et prévention, de A.Whitaker, K.Evans, J.Vot

    Bonjour La rédaction de DVP a lu pour vous l'ouvrage suivant: Chaînes d'exploits: Scénarios de hacking avancé et prévention de Andrew Whitaker, Keatron Evans, Jack Voth paru aux Editions PEARSON [IMG]http://images-eu.amazon.com/images/P/274402371X.08.LZZZZZZZ.jpg[/IMG] Citation: Un pirate informatique s'appuie rarement sur une unique attaque, mais utilise plutôt des chaînes d'exploits, qui impliquent plusie...

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  • SQL Injection prevention

    - by simonsabin
    Just asking people not to use a list of certain words is not prevention from SQL Injection https://homebank.sactocu.org/UA2004/faq-mfa.htm#pp6 To protect yourself from SQL Injection you have to do 1 simple thing. Do not build your SQL statements by concatenating values passed by the user into a string an executing them. If your query has to be dynamic then make sure any values passed by a user are passed as parameters and use sp_executesql in TSQL or a SqlCommand object in ADO.Net...(read more)

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  • Data Execution Prevention in Windows Live Messenger

    - by Andrija
    I keep getting "Data Execution Prevention" error in Windows Live Messenger. I have noticed that this is happening usually when I leave computer to get coffee, and screensaver comes up, WLM breaks. Is there any way to prevent this error from happening? I see I can turn off this "Data Execution Prevention", but is that safe, since I know that WLM is under heavy attacks from spammers/hackers? Thanks

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  • DNS hijack - prevention tips

    - by user578359
    Hi there, Over the weekend it looks like the DNS was hijacked on two of my domains. My set up is I have the sites registered on 1and1.co.uk, with dns nameservers pointing to Hostgator in the US where the sites are hosted. I also had cloudflare CDN running on the sites (via hostgator cpanel). My question is any ideas as to how this happened, and how I could either monitor it so I know if it occurs again, or strengthen the set up/service to minimise the risk. History: I received a ping from my site monitoring service that the sites were down. When I checked the sites were up so I assumed it was local to the monitoring service I received a ping last night the sites were up When I checked, one site was redirecting to download-manual.com (and checking that URL now, the home page is not the same as the one I saw, so they too may have been hijacked/hacked) The other site URL remained the same but had one of those standard site search pages which bounce you off to either phishing or paid for search sites I notified Hostgator who told me Cloudflare or 1and1 were the issue. I removed cloudflare, and contacted both them and hostgator, and am awaiting a response, but am not holding my breath. Is this common? I've never heard of this or come across this before. It's pretty scary that this can happen so easily. Appreciate any input. **Update: I've now spoken to support at 1and1, Hostgator, and Cloudflare, and each one claims it has nothing to do with them, and must be one of the others. Larry, curly, moe.

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  • Sql Injection Prevention

    To protect your application from SQL injection, perform the following steps: * Step 1. Constrain input. * Step 2. Use parameters with stored procedures. * Step 3. Use parameters with dynamic SQL.

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