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Date: April 29, 2024 Mon
Time: 8:07 pm
Time: 8:07 pm
Results for network analysis
4 results foundAuthor: Davis, Ian S. Title: A Theory of Dark Network Design Summary: This study presents a theory of dark network design and answers two fundamental questions about illuminating and interdicting dark networks: how are they configured and how are they vulnerable? We define dark networks as interdependent entities that use formal and informal ties to conduct licit or illicit activities and employ operational security measures and/or clandestine tradecraft techniques through varying degrees of overt, or more likely covert, activity to achieve their purpose. A dark network must design itself to buffer environmental hostility and produce output to achieve its purpose according to its design state. The level of hostility in the environment and the requirement for secure coordination of work determine the dark network’s design state. These factors yield four typological dark network configurations: Opportunistic-Mechanical; Restrictive-Organic; Selective-Technical; and Surgical-Ad hoc. Each configuration must allow the secure coordination of work between the dark network’s directional, operational, and supportive components and should adhere to the six principles of dark network design we identify: security, agility, resilience, direction setting, control, and capacity. If a dark network’s configuration does not fit its design state or violates the principles of dark network design, the network will be vulnerable to illumination and interdiction. Details: Monterey, CA: Naval Postgraduate School, 2010. 177p. Source: Iinternet Resource: Thesis: Accessed October 9, 2012 at: http://edocs.nps.edu/npspubs/scholarly/theses/2010/Dec/10Dec_Davis_Ian.pdf Year: 2010 Country: International URL: http://edocs.nps.edu/npspubs/scholarly/theses/2010/Dec/10Dec_Davis_Ian.pdf Shelf Number: 126650 Keywords: Criminal NetworksCriminal SyndicatesHezbollahMara Salvatrucha-13Network AnalysisProvisional Irish Republican ArmyTerrorism |
Author: Fellman, Philip V. Title: Disrupting Terrorist Networks - A Dynamic Fitness Landscape Approach Summary: The study of terrorist networks as well as the study of how to impede their successful functioning has been the topic of considerable attention since the odious event of the 2001 World Trade Center disaster. While serious students of terrorism were indeed engaged in the subject prior to this time, a far more general concern has arisen subsequently. Nonetheless, much of the subject remains shrouded in obscurity, not the least because of difficulties with language and the representation or translation of names, and the inherent complexity and ambiguity of the subject matter. One of the most fruitful scientific approaches to the study of terrorism has been network analysis (Krebs, 2002; Carley, 2002a; Carley and Dombroski, 2002; Butts, 2003a; Sageman, 2004, etc.) As has been argued elsewhere, this approach may be particularly useful, when properly applied, for disrupting the flow of communications (C4I) between levels of terrorist organizations (Carley, Krackhardt and Lee, 2001; Carley, 2002b; Fellman and Wright, 2003; Fellman and Strathern, 2004; Carley et al, 2003; 2004). In the present paper we examine a recent paper by Ghemawat and Levinthal, (2000) applying Stuart Kauffman's NK-Boolean fitness landscape approach to the formal mechanics of decision theory. Using their generalized NK-simulation approach, we suggest some ways in which optimal decision-making for terrorist networks might be constrained and following our earlier analysis, suggest ways in which the forced compartmentation of terrorist organizations by counter-terrorist security organizations might be more likely to impact the quality of terrorist organizations' decision-making and command execution. Details: Unpublished Paper, 2007. 13p. Source: Internet Resource: Accessed March 26, 2016 at: http://arxiv.org/ftp/arxiv/papers/0707/0707.4036.pdf Year: 2007 Country: International URL: http://arxiv.org/ftp/arxiv/papers/0707/0707.4036.pdf Shelf Number: 138424 Keywords: Criminal NetworksDecision MakingNetwork AnalysisTerrorismTerrorists |
Author: Magalingam, Pritheega Title: Complex network tools to enable identification of a criminal community Summary: Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal association from enormous amount of raw data and representing them explicitly is tedious and time consuming. A study of the complex networks literature reveals that graph-based detection methods have not, as yet, been used for money laundering detection. In this research, I explore the use of complex network analysis to identify the money laundering criminals' communication associations, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path. For this purpose, I use the publicly available Enron email database that happens to contain the communications of 10 criminals who were convicted of a money laundering crime. I show that my new shortest paths network search algorithm (SPNSA) combining shortest paths and network centrality measures is better able to isolate and identify criminals' connections when compared with existing community detection algorithms and k-neighbourhood detection. The SPNSA is validated using three different investigative scenarios and in each scenario, the criminal network graphs formed are small and sparse hence suitable for further investigation. My research starts with isolating emails with 'BCC' recipients with a minimum of two recipients bcc-ed. 'BCC' recipients are inherently secretive and the email connections imply a trust relationship between sender and 'BCC' recipients. There are no studies on the usage of only those emails that have 'BCC' recipients to form a trust network, which leads me to analyse the 'BCC' email group separately. SPNSA is able to identify the group of criminals and their active intermediaries in this 'BCC' trust network. Corroborating this information with published information about the crimes that led to the collapse of Enron yields the discovery of persons of interest that were hidden between criminals, and could have contributed to the money laundering activity. For validation, larger email datasets that comprise of all 'BCC' and 'TO/CC' email transactions are used. On comparison with existing community detection algorithms, SPNSA is found to perform much better with regards to isolating the sub-networks that contain criminals. I have adapted the betweenness centrality measure to develop a reliance measure. This measure calculates the reliance of a criminal on an intermediate node and ranks the importance level of each intermediate node based on this reliability value. Both SPNSA and the reliance measure could be used as primary investigation tools to investigate connections between criminals in a complex network. Details: Melbourne: School of Mathematical and Geospatial Sciences, College of Science, Engineering and Health, RMIT University, 2015. 129p. Source: Internet Resource: Dissertation: Accessed May 13, 2017 at: https://researchbank.rmit.edu.au/eserv/rmit:161407/Magalingam.pdf Year: 2015 Country: Australia URL: https://researchbank.rmit.edu.au/eserv/rmit:161407/Magalingam.pdf Shelf Number: 145462 Keywords: Criminal InvestigationCriminal NetworksGeospatial AnalysisMoney LaunderingNetwork AnalysisOrganized Crime |
Author: Marshak, Charles Zachary Title: Applications of Network Science to Criminal Networks, University Education, and Ecology Summary: Networks are a powerful tool to investigate complex systems. In this work, we apply network{theoretic tools to study criminal, educational, and ecological systems. First, we propose two generative network models for recruitment and disruption in a hierarchal organized crime network. Our network models alternate between recruitment and disruption phases. In our first model, we simulate recruitment as Galton-Watson branching. We simulate disruption with an agent that moves towards the root and arrests nodes in accordance with a stochastic process. We prove a lower bound on the probability that the agent reaches the kingpin and verify this numerically. In our second model, we propose a network attachment mechanism to simulate recruitment. We define an attachment probability based on an existing node's distance to the leaf set (terminal nodes), where this distance is a proxy for how close a criminal is to visible illicit activity. Using numerical simulation, we study the network structures such as the degree distribution and total attachment weight associated with large networks that evolve according to this recruitment process. We then introduce a disruptive agent that moves through the network according to a self-avoiding random walk and can remove nodes (and an associated subtree) according to different disruption strategies. We quantify basic law enforcement incentives with these different disruption strategies and study costs and eradication probability within this model. In our next chapter, we adapt rank aggregation methods to study how Mathematics students navigate their coursework. We first translate 15 years of grade data from the UCLA Department of Mathematics into a network whose nodes are the various Mathematics courses and whose edges encode the flow of students between these courses. Applying rank aggregation on such networks, we extract a linear sequence of courses that reflects the order students select courses. Using this methodology, we identify possible trends and hidden course dependencies without investigating the entire space of possible schedules. Specifically, we identify Mathematics courses that high-performing students take significantly earlier than low-performing students in various Mathematics majors. We also compare the extracted sequence of several rank aggregation methods on this data set and demonstrate that many methods produce similar sequences. In our last chapter, we review core-periphery structure and analyze this structure in mutualistic (bipartite) fruigivore-seed networks. We first relate classical graph cut problems to previous work on core-periphery structure to provide a general mathematical framework. We also review how core-periphery structure is traditionally identified in mutualistic networks. Next, using a method from Rombach et al., we analyze the core-periphery structure of 10 mutualistic fruigivore-seed networks that encode the interaction patterns between birds and fruit-bearing plants. Our collaborators use our network analysis with other ecological data to identify important species in the observed habitats. In particular, they identify certain types of birds (mashers) that play crucial roles at a variety of sites, which are though to be less important due to their feeding behaviors. Details: Los Angeles: University of California, 2016. 184p. Source: Internet Resource: Dissertation: Accessed July 27, 2017 at: http://escholarship.org/uc/item/6zs7394q Year: 2016 Country: United States URL: http://escholarship.org/uc/item/6zs7394q Shelf Number: 146588 Keywords: Criminal NetworksIllicit ActivityNetwork Analysis |