On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Destabilization of covert networks

Covert networks are often difficult to reason about, manage and destabilize. In part, this is because they are a complex adaptive system. In addition, this is due to the nature of the data available on these systems. Making these covert networks less adaptive, more predictable, more consistent will make it easier to contain or constrain their activity. But, how can we inhibit adaptation? Herein, covert networks are characterized as dynamic multi-mode multi-plex networks. Dynamic network analysis tools are used to assess their structure and identify effective destabilization strategies that inhibit the adaptivity of these groups.     

Micellar electrokinetic chromatography of organic and peroxide-based explosives

CE methods have been developed for the analysis of organic and peroxide-based explosives. These methods have been developed for deployment on portable, in-field instrumentation for rapid screening. Both classes of compounds are neutral and were separated using micellar electrokinetic chromatography (MEKC). The effects of sample composition, separation temperature, and background electrolyte composition were investigated. The optimised separation conditions (25 mM sodium tetraborate, 75 mM sodium dodecyl sulfate at 25 °C, detection at 200 nm) were applied to the separation of 25 organic explosives in 17 min, with very high efficiency (typically greater than 300,000 plates m⁻¹) and high sensitivity (LOD typically less than 0.5 mg L⁻¹; around 1–1.5 μM). A MEKC method was also developed for peroxide-based explosives (10 mM sodium tetraborate, 100 mM sodium dodecyl sulfate at 25 °C, detection at 200 nm). UV detection provided LODs between 5.5 and 45.0 mg L⁻¹ (or 31.2–304 μM), which is comparable to results achieved using liquid chromatography. Importantly, no sample pre-treatment or post-column reaction was necessary and the peroxide-based explosives were not decomposed to hydrogen peroxide. Both MEKC methods have been applied to pre-blast analysis and for the detection of post-blast residues recovered from controlled, small scale detonations of organic and peroxide-based explosive devices.     

A quantitative assessment of chemical techniques for detecting traces of explosives at counter-terrorist portals

Previous reviews have discussed in a qualitative manner the various highly sensitive analytical techniques for detecting minute traces of explosive material. However, there is no review available which compares quantitatively the sensitivities of the different analytical methods for detecting explosives. In view of the importance of this area to the present day planning of counter-terrorist strategies, this review makes a comprehensive and quantitative comparison of the analytical chemical methods which can be used for the detection of trace explosives in the luggage and on the persons of travelers. Possible directions of future development in this area are also discussed.