Data Mining for Intelligence, Fraud & Criminal Detection: by Christopher Westphal

By Christopher Westphal

In 2004, the govt. responsibility place of work supplied a record detailing nearly two hundred government-based data-mining tasks. whereas there's convenience in realizing that there are numerous powerful structures, that convenience isn’t worthy a lot until we will make sure that those structures are being successfully and responsibly employed.

Written through essentially the most revered specialists within the sector of information mining and security, facts Mining for Intelligence, Fraud & felony Detection: complicated Analytics & details Sharing Technologies reports the tangible effects produced via those platforms and evaluates their effectiveness. whereas CSI-type indicates could depict details sharing and research which are finished with the frenzy of a button, this type of skillability is extra fiction than fact. Going past a dialogue of a number of the applied sciences, the writer outlines the problems of knowledge sharing and the potent interpretation of effects, that are serious to any built-in fatherland safeguard attempt.

Organized into 3 major sections, the ebook absolutely examines and descriptions the way forward for this box with an insider’s viewpoint and a visionary’s perception.

  • Section 1 offers a basic figuring out of the kinds of information that may be utilized in present structures. It covers methods to examining info and obviously delineates the best way to attach the dots between varied facts elements
  • Section 2 offers real-world examples derived from real operational structures to teach how info is used, manipulated, and interpreted in domain names concerning human smuggling, cash laundering, narcotics trafficking, and company fraud
  • Section three offers an summary of the various information-sharing structures, enterprises, and activity forces in addition to facts interchange codecs. It additionally discusses optimum information-sharing and analytical architectures

Currently, there's little or no released literature that really defines real-world platforms. even though politics and different elements all play into how a lot one enterprise is keen to help the sharing of its assets, many now include the knowledge of that direction. This booklet will offer these people with an knowing of what ways are at the moment on hand and the way they are often so much successfully hired.

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Additional resources for Data Mining for Intelligence, Fraud & Criminal Detection: Advanced

Example text

Usually, there is a known starting point from a past crime, arrest, or some type of situation. This type of investigation typically represents a “reactive” situation. Reactive analyses are based on the preselection of an entity, such as a person (as in this case), organization, account, location, shell casing, DNA sample, or criminal event. The entity of interest is already known and becomes the center, or focus, of the analysis. Ultimately, the goal of a reactive analysis is to expand on the known network to find additional clues and leads where the investigator would look at all aspects of the subject to determine other people who are related to him or her through family, business dealings, criminal records, or any other source, to show unusual connections or associations that might expose important connections to other criminal activities.

Additional searches to try and identify any associates, gang members, or family members living at the same address come up negative from the criminal database. Thus, there is no other information in this particular source that will further extend the network. However, because this agency has access to other sources of data, the investigator crossreferences all of the information with another online source. In this example, the phones, ID numbers, and addresses are checked against a federal database containing Suspicious Activity Reports (SARs) filed by banks, financial institutions, casinos, and money service businesses (MSBs— see sidebar) throughout the United States.

The ROI (return on investment) for information sharing is immediate, significant, and measurable. Conclusion The analytical community is changing every day. New methods, approaches, and technologies are being applied to help improve how data is accessed, combined, analyzed, and reported. This is a never-ending and constantly changing paradigm where new techniques and methods must be developed to keep pace with the threats that emerge every day. Terrorism has changed how governments and businesses operate, and our adversaries are constantly changing how they will plan and execute the next attack.

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