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Investigative Data Mining: Mathematical Models for Analyzing, Visualizing and Destabilizing Terrorist Networks

von Nasrullah Memon

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[1.] Nm/Fragment 026 01 - Diskussion
Zuletzt bearbeitet: 2012-04-29 22:21:43 Hindemith
BauernOpfer, DeRosa 2004, Fragment, Gesichtet, Nm, SMWFragment, Schutzlevel sysop

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Quelle: DeRosa 2004
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[This technique uses aggregated public records or other large collection of data to find the links between a subject—a suspect, an address,] or a piece of relevant information—and other people, places, or things. This can provide additional clues for analysts and investigators to follow (DeRosa Mary, 2004). This technique uses aggregated public records or other large collections of data to find links between a subject—a suspect, an address, or other piece of relevant information—and other people, places, or things. This can provide additional clues for analysts and investigators to follow.
Anmerkungen

continuation of previous fragment

Sichter
(Graf Isolan), Bummelchen

[2.] Nm/Fragment 026 04 - Diskussion
Zuletzt bearbeitet: 2012-05-03 21:09:50 Hindemith
Fragment, Gesichtet, KomplettPlagiat, Koschade 2005, Nm, SMWFragment, Schutzlevel sysop

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IDM also gives the analyst the ability to measure the level of covertness and efficiency of the cell as a whole, and the level of activity, ability to access others, and the level of control over a network each individual possesses. The measurement of these criteria allows specific counter-terrorism applications to be drawn, and assists in the assessment of the most effective methods of disrupting and neutralising a terrorist cell. In short, IDM provides a useful way of structuring knowledge and framing further research. Ideally it can also enhance an analyst’s predictive capability (Memon N., and Larsen H. L., 2006c). The method also endows the analyst the ability to measure the level of covertness and efficiency of the cell as a whole, and also the level of activity, ability to access others, and the level of control over a network each individual possesses. The measurement of these criteria allows specific counter-terrorism applications to be drawn, and assists in the assessment of the most effective methods of disrupting and neutralising a terrorist cell.

[page 3]

In short, social network analysis “provides a useful way of structuring knowledge and framing further research. Ideally it can also enhance an analyst’s predictive capability”.[EN 12]


[EN 12] Aftergood, S. (2004) ‘Secrecy News: Social Network Analysis and Intelligence’ [online], Federation of American Scientists Project on Government Secrecy, Vol. 2004, 15. Retrieved May 17, 2004, from http://www.fas.org/sgp/ news/secrecy/2004/02/020904.html.

Anmerkungen

The source is not mentioned anywhere in the thesis.

Not only did Nm copy an entire paragraph from the source, he also references for it the paper "Memon N., and Larsen H. L., 2006c", written by himself and the thesis supervisor. Finally he also removes the reference to Aftergood (2004) who Koschade correctly quotes for the last sentence.

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(Hindemith), Bummelchen

[3.] Nm/Fragment 026 14 - Diskussion
Zuletzt bearbeitet: 2012-04-28 16:28:06 Hindemith
Fragment, Gesichtet, Nm, Popp and Poindexter 2006, SMWFragment, Schutzlevel sysop, Verschleierung

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Quelle: Popp and Poindexter 2006
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Traditional data mining normally refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence, machine learning, fuzzy logic, statistics to examine through large amounts of data to find previously unknown but statistically significant patterns. However, the application of IDM in the counterterrorism domain is more challenging, because unlike traditional data mining applications, we must find extremely wide variety of activities and hidden relationships among individuals (Seifert 2006). Table 1.1 gives a series of reasons why traditional data mining is not the same as investigative data mining. Data mining commonly refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence to comb through large amounts of data to discover previously unknown but statistically significant patterns. However, the general counterterrorism problem is much harder because unlike commercial data mining applications, we must find extremely rare instances of patterns across an extremely wide variety of activities and hidden relationships among individuals. Table 2 gives a series of reasons for why commercial data mining isn’t the same as terrorism detection in this context.
Anmerkungen

no source given

Sichter
(Graf Isolan) Agrippina1


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