iframe and cross-domain security leads to "Bahama BotNet"?

Click Forensics is calling this the Internet marketing potential fraud behavior - "Bahama botnet" because initially it was redirecting traffic through 200,000 parked domains in the Bahamas, although it now is using sites in Amsterdam, the U.K. and Silicon Valley.

HTML tag Iframes can be very flexible too and is said to be potential source for the fraud - "Bahama botnet"; much less constrained than a "framed" page. They can be a great way to add an "update" section to a page without having to worry about the size of the new content.

Iframes place a smaller "box" containing another HTML document inside the larger main display. It's like having a smaller window inside the main window to display a separate source of information. Frames split an entire window into two or more sections. Frames run edge to edge rather than being a box placed somewhere inside.

Click fraud affects marketers who spend money on pay-per-click (PPC).


Sophisticated Botnet Causing a Surge in Click Fraud

Study: Half Of Ad Impressions, 95 Percent Of Clicks Fraudulent

NYtimes.com Ad Scam Linked to 'Bahama' Botnet

Click Fraud's New Asian Connection

Yahoo! Cozies Up To Its Click-Fraud Critics

Click Fraud Goes Viral

Google Defends Its Clicks

Note: First introduced by Microsoft Internet Explorer in 1997 and long only available in that browser, the iframe tag is now widely supported by visual browsers. Unlike an object element, an inline frame may be the "target" frame for links defined in other elements and it may be "selected" by a browser as the focus for printing, viewing HTML source etc.

BI can lay stepping stone for Fraud Analytics!

Fraud losses can impact every business. Fraud Analytic differs from Business Intelligence(BI) type Analytic with relatively higher human interaction and deep dive, but it's all about data. Understanding of data and visual representation can provide early alert, enabling users to take timely action to stop fraud and halt losses. A proactive approach will combat fraudulent behavior and increase the perceived value of the organization and help to the cause of Customer loyalty, competitive edge in dynamic market place, merger and acquisition so on so forth ...

Information Life-cycle and evolution: Yes, Information has a lifecycle. Information derived from data should be following the information supply chain. Data becomes information when it represents business relationships. Data should be aligned to business model and process such that precise insight can be represented of customer behavior, how much acceptable risk can be taken and so on. Organization of information should be able to cater to wide range of strategies covering definition, policy, infrastructure and operation and functionality. For example - Data is born when customer places an order, which gets associated to identification definitions like customer, product, market .. Then goes thru order fulfillment and then customer service repair so on so forth. The data in the meantime goes through various transformations as it is related to financial, marketing, demand planning or predictive uses, in order to answer questions crucial to operating and optimizing business decisions. Finally, information has an end game. The customer moves away or the product is discontinued. The data is no longer updated, remains unused by the business and eventually become irrelevant both to the enterprise and the society in which the enterprise does business. As the volumes of data accumulate, the data warehouse becomes “obese.” Meanwhile, the data warehouse become entwined with mission-critical systems, impacting the performance of both transactional and decision support systems. This drive information mining with stale information and more resources to deep dive into a slice of information set for confirming the outliers found in the initial data set.

Managing Your Data Growth: A system that has been in production for several years is likely to contain a significant volume of data that is not used at all or used infrequently. Data warehouses often start big and get even bigger. The lifecycle of the data warehouse and the requirement to perform archiving shifts into the foreground. Enterprises engage in data archiving as part of an approach to information lifecycle management, of which data warehousing is an essential part. Archiving is the best way both to improve performance of the data warehouse (or transactional system) and to satisfy the requirements for data retention and security. This will enable improved ROI, information richness and better response when reaching for active and/or inactive data.

Visual representation of Information for Discovery: The human brain is good at doing some things and limited in others. For example, our brains is good at recognizing visual patterns, while they are able to remember relatively little from large amount of information. Primarily data analysis is making sense using comparison as individual facts mean nothing by themselves. Facts become meaningful when we compare them to one another. By displaying data in series of small graphs arranged as a visual cross-tab, which allows multiple dimensions to be compared simultaneously. This will allow users to see and compare patterns and trends of outlier or inconsistent behavior. Thus provide potential fraud candidate for further investigation.

Conforming Architecture: Fraud identification has a different approach than general Data warehouse or BI solution. In case of general BI solution, the measurement criteria is often a set of transaction quantifying success of a campaign or Sales Measures so on, however fraud would taking a subset of data and analyze association with scenarios by means of data and deep dive. As BI can provide model based information of trends, human interaction will identify the outlier cases for further investigation. Mature Business Intelligence architecture need to consider the commonalities and differentiators in their architecture to cater to these different audience needs. In addition, the deployment design of information architecture if not flexible enough then there will be high cost of reaching to the tip of the iceberg beyond that potential could be more investment for each scenario i.e unsustainable spiral.

Holistic approach: Fraud Management Lifecycle is dynamic, evolving, and BI solution architectures should be flexible enough to adaptive it. Effective fraud management requires a balance in the competing and complementary actions within the Information Lifecycle. Solutions can be defined based on past data trend, but the power of success lies with solutions that can embrace the new data to provide the insight.

Consolidation has left a lot of companies with multiple incompatible systems, inconsistent applied policies, more holds and less penetration in dynamic market place. Even with mature BI organization, fraud analytic can only be effective with ability to efficiently link with different data set and robust architecture.

Reference: Journal of Economic Crime Management
Statistics: The Fraud Management Lifecycle Theory

An Approach to actionable Search Result Listing

Search results are often a list of relevant matching criteria based on interpretation of searched phrase by the search engine. This is often due to the fact that the search engines do not really know their customer's profile or Savvy level on the subject they are searching, but based on best probability relevance! Hence the result set is driven by the best choices the search engine can produce based on the algorithm the engineers have evolved. Perhaps it's time for better marriage of Web 2.0 interface and the search engines.

When I look at the content searching web sites, there are number of them and each of them emphasizes on the efficiency of their search engine and methodology, but not much is done on the presentation of the information back to the user. An approach could be leveraging the context in the search when building the Thesaurus-based Search indexing with attributes from the users and then displaying in a catalog mode with relevant contextual information. Example - An user from Southern India looking for math School books for grade 7th would be different than a user who is searching for the same thing in Tokyo or London. Showing results that provides additional dimension like - what are other people in the area has higher click when they are searching with similar phrase or if an teacher is searching from Tokyo what content has higher hit vs when a student is searching with the same phrase. Perhaps user defining their profile to assist the search engine to provide more actionable relevant results with dimensions to drill-down to the content or information in their search results that they believe is more relevant to them and save resources to render the content not relevant to them. This approach could enable users to reach their content relatively quickly.

Think about it, if you are an advanced golfer, looking for golf clubs, possibility would be high that they are probable looking for fine quality clubs, thus the search result can list dimension with high value to the professionals like specification, Price range, Type of Clubs, Purpose, How would they buy so on so forth so that the messaging can be more focused bringing in the sellers, Bloggers, eCommerce site, so on so forth together for a sweeter user experience and Web 2.0 interfacing is key to this experience. This can potentially assist eCommerce vendors to re-align their inventory hubs for shorter turn around in responding to the customer and their usage patterns. For example, eBay sellers would benefit from this information to message their product listing in such a way that messaging is focused for their targeted customer and listing their products in the appropriate category, listing price, more value to their customer so on so forth ...

Now, this approach will initiate a whole new world of search service for various other service companies who can package the results for a specific target audience. Search companies can also gain some extra money by selling these services for the indexing that they have already done for their bread and butter. This will also promote a new ecosystem of collaboration in internet marketing and interesting application with vertical searching industry leveraging content.

I believe this will promote reduction in replication of content as well and contribute to the cause of green ecology for future. It's time for consuming more of Web 2.0 interface into the higher value internet applications for more actionable search results.

Reference:
1. Kosmix: It is Wikipedia meets Google search meets Answers.com
2. MeeHive: It is an interesting cross between Google News, Google Reader, and social news sites like socialmedian
3. NewsGator: Inbox for Outlook integrating RSS feeds (and Usenet news) with an email client. NewsGator lets you read, archive, organize and search news with all the power of Outlook

Analytic Solution - Prone to degrading performance?

As the desire for the enterprise to act quickly and navigate through the changing markets, this has mushroomed new generation of analytic solution to deal with the data growth and desire for discovering new insight. In past many years, I have noticed that when defining the solution for decision support, the first step has often been to build a quick solution without worrying about building sustainable infrastructure. But, soon these solutions starts hitting the wall, as they were either highly customized and or built with the least costly option!

As analytic applications are driving for prime time, organizations are faced with hard facts and consequences of their past decisions. With this increase of focus, core of Analytic solution for enhanced ROI needs to be built on the Enterprise information integration with tomorrow's needs in mind.

Capability to scale as system grows in size:
Pay attention to database, platform and network for handling of I/O for an acceptable through-put. Each system should be individually understood to understand the scalability choices to act appropriately to address the effect of growth. Especially for Analytic solutions, data integration and analytic processing are another choking point due to need for extraction of information from large data set effectively and efficiently. Effective management ingredients are key to the scalable solution.

Throwing more horse-power such that increasing processing power, more indexes so on, such brute force are short term solution before next crisis emerges. Managing scalability begins with defining an architecture that can start small but grow overtime in coordination with each component in the architecture physical or logical.

Often the case is that the actual usage facts/statistics are obtained after first couple of months of the solution use, hence most of the assumptions for usage should be critically weighed and considered. This should include the headroom for growth of physical architecture with consideration for multi-thread processing in all stages from migration to everyday operation.

Logical Architecture should ensure deployment mode with a clear understanding for what is required for tactical vs strategic decision making - when, what, where and who.

Poor Quality Data and Obsolete Content:
Managing the growth based on usage, subject focus, analytical and reporting capability can overcome costly engineering efforts under the umbrella of growth consequence. Emphasizing on the effort to capture the appropriate information (meta-data) when the data is created. Associated meta-data from the beginning would be crucial for the need of the information for decision making. Master Data management should be embedded and evolving process. Competitive advantage will be gained in the continued evaluation and improvement of the matching accuracy and trust worthy data and critical resource juice while processing at various stages.

Transforming the data:
Understand the infrastructure required to perform the ETL and ad-hoc front query is key to needs for tactical and strategic analytic needs. The stress on the transactional systems and analytic solutions are very different, eg. unlike transactional system, analytic solutions a based on utilization rates and scope of data, so on. In addition query parameters would vary quiet sharply leading to large data set. Addressing this would be a balancing act based on the need like freshness of the data, business events, security and privacy, aggregated view vs detail/trend view. Investment should not be purely dominated by tools, but in coordination of user's needs, experienced and balanced deployment team.

Operation Management:
Monitoring and preventing the bottleneck situation or system crash should be prime role of operation's team for today's global systems with ever tightening processing window. In addition, coordination with network management to deal with insufficient bandwidth, but provide acceptable system performance is critical to success of the solution. Console view of the operations with ability to highlight the alerts with defined threshold will enable the systems not to be in crisis mode.

Staging Data:
Key consideration should be given to separation of user experience from physical infrastructure. Semantic layer hides the complexity of underlying data sources, by providing a business representation of organizational data. The semantic layer also makes reusable report elements and powerful calculation capabilities available, allowing users to quickly access key information. In addition, you can use the semantic layer's metadata throughout your organization's BI solutions. Semantic layer should be supported by Enriched data layer for summary, details and slowly changing dimension. Deploying the KPIs at semantic layer vs at database level often causes the data fetch performance. Planning each layer with usage understanding, auditing and monitoring will enable system efficiency.

Change Management:
The pipeline of the changes should be sequenced such that it differentiates the core changes required for deploying an front end change. Deploying the foundational back-end elements and build on top of it to deliver the front end functionality will support the effort for the optimal return and scalability.

Scalability should be planned for, as early as pilot project phase to cope with consequences of growth.

Finding Fraud with analytics

Fraud analytics starts with a theory. Theory has assumptions and some gut factor. Think about it, all our life from childhood to adult life we are involved in search-n-seek. Childhood days it's cookies, candies and so on, but as we grow up car keys, glasses, remote so on so forth; however the point to note is that the strategy to find things constantly evolves. Perhaps, to find fraud in business process and systems, the search-n-find skill need to be taken to the next level.

The detection strategy should be such that is proactive and constantly audited. But, based on experience one need to outline some assumptions to identify trees in the forest. The approach to find the tree in the forest should be such that it can be modeled and be repetitive processes. These models and processes can be deployed by IT teams for business to monitor and evolve the pattern. Sounds simple, but a properly designed fraud plan begins with simply looking for instances where a fraud scenario is most likely to occur, much like a search-and-find game.

Effective fraud plan also requires awareness, or the ability to interpret the data for the indicators, of the fraud scenario. While the simple fraud scenarios can be detected via a properly designed fraud data procedure, a fraud scenario with a sophisticated concealment strategy requires the ability to see through the concealment strategy.



1. List your assumptions based on high probable cases. The key considerations are to
understand the variations of the scenario that are caused by the fraud opportunity. This helps define the scope of the Fraud Audit.

2. Develop a fraud data profile with data, using the process of drawing a picture of a fraud scenario. For example, one variation of a false billing scheme through a false company is when the accounts payable takes over the identity of a dormant vendor on the database and charges invoices to a large cost center.

3. Structured step-by-step approach to identifying transactions consistent with a fraud scenario/assumption, as described through the fraud data profile.

4. Obtain pertinent data and their relation to the assumption.

5. Define data interrogation procedure - pattern & Frequency, identify outlier cases for good and bad both, Trends, GAP in business process, potential mistakes in data capture and transactional history, Master data accuracy

6. Define the KPI (Key performance Indicator) and monitor the indicators regularly

7. Prepare plan to respond to the indicator pattern. Evolve the KPIs for further sophistication and insight.

8. Once the culprits is identified monitor their behavior for firming up the plan. This will also help evolve the good vs bad outliers.

Search routines help focus identifying of “red flags” of the fraud scenario/assumption. By using data interpretation, one can develop reports or documentation and interpret the data.

Facts:
Insurance: In United States, about $67 billion is lost every year to fraudulent claim.(Federal Bureau of Investigation [FBI], 2003).
Telecommunications: $1.5 trillion phone industry loses approximately 10% to fraud, that is $150 billion at current estimates (Mena, 2003).

Bank Fraud: For the period of April 1, 1996 through September 30, 2002, the FBI received 207,051 Suspicious Activity Reports equaled approximately $7 billion in losses (U.S. Department of Justice [DOJ], 2002).

Money Laundering: United States Treasury officials estimate that as much as $300 billion is laundered annually, worldwide, with from $40 billion to $80 billion of this originating from drug profits made in the United States. (Mena, 2003).

Internet: According to Meridien Research, worldwide credit card fraud[the Internet component] will represent $15.5 billion in losses [annually] by 2005. However, if merchants adopt data mining technology now to help screen credit-card orders prior to processing, the widespread use of this technology is predicted to cut overall losses by two thirds to $5.7 billion in 2005” (Mena, 2003).

Credit Card: The numbers from the Nilson report indicate that issuer credit card fraud losses run approximately 1 billion dollars annually. This list does not even include debit card fraud, brokerage fraud, fraud at casinos, health care fraud, and other miscellaneous fraud types such as bankruptcy fraud
Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2

Senator Everett Dirksen so aptly said, “A billion here a trillion there; the first thing you know, you’re talking about real money.”

Source: Journal of Economic Crime Management Spring 2004, Volume 2, Issue 2

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