Disrupting Improvised Explosive Device Terror Campaigns Basic Research Opportunities: A Workshop Report (2008) / Chapter Skim
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3 PREDICTING IMPROVISED EXPLOSIVE DEVICE ACTIVITIES (WORKSHOP 2)
3 PREDICTING IMPROVISED EXPLOSIVE DEVICE ACTIVITIES (WORKSHOP 2)
Pages 30-47
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From page 30... ...
They were delivered by experts in law enforcement, computer science, statistics, mathematics, and remote sensing. The breakout sessions gave participants a chance to explore the kinds of data needed to predict IED activities and kinds of basic research that would enable the handling, priority-setting, and delivery of such data; that would allow leveraging of human expertise in data interpretation; and that might lead to procedures for analyzing mixed, complex, noisy, or incomplete data.
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To commit fraud, they use a wide array of technologies to exploit weaknesses at the interfaces of technology and 4 Department of Homeland Security: START Global Terrorism Database. http://www.start.umd.edu/data/gtd/.
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Fraudsters will migrate to the telecommunication provider that is easiest to do business with, that is, the provider that is easiest to defraud. Similarly, insurgents and terrorists C tend to attack the targets that are the easiest to strike, although sometimes they choose targets for their symbolic importance or for other reasons.C The problem of fraud detection in the case of telephone networks is an example of how massive datasets can be analyzed.
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DEPLOYING WIRELESS SENSOR NETWORKS FOR ENVIRONMENTAL SENSING Alex Szalay (Johns Hopkins University) discussed how lessons learned from his experience deploying wireless sensor networks for environmental sensing might be useful in countering IEDs.
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Data fusion is the acquisition, processing, and synergistic combination of data gathered by various sources and sensors to improve the understanding of some phenomenon or to introduce or enhance intelligence and system control functions. An example of data fusion is the processing used by the human brain, which naturally fuses human sensory information to make inferences regarding the environment.
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. Note that data fusion is particularly relevant to information obtained from wireless sensor networks.
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explained how reflexivecontrol theory, originally developed by Vladimir Lefebvre, could be used in a counterIED context. In traditional mathematical approaches, a person trying to deter IED activities -- the decision-maker -- is passive and thus does not take into account his or her ability to preemptively influence an adversary's actions.
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However, predictive models have been developed for other complex, high-dimensional signal processing problems -- for example, in telecommunication, "electronic nose" sensor arrays, Internet traffic, and genetics networks -- and the results may be applicable to IED discovery. Developing an analytic method for discovery in a complex system requires observations from the field and relevant contextual information to build a function that can be used to estimate or predict the state of the system.
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For example, detecting anomalies in internet traffic data has an important parallel to IED detection. Internet traffic has a constant baseline shift: at no two times will the volume of internet traffic be exactly the same.
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The final session of the workshop built on the talks and breakout sessions. Participants were invited to provide feedback on overarching themes and critical research subjects highlighted during the workshop.
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Modeling may also help to compensate for errors resulting from those incomplete datasets, and may reduce the analysis required by identifying the most and least valuable portions of the collected data. Modeling may also help to develop proxy datasets for testing of analytic methods.
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Some basic research subjects are promising, such as developing improved methods of image identification or modeling of networks and informal financial systems, but application of the basic research to IED activities will require access to pertinent datasets. Research Needed to Leverage Human Expertise in Data Interpretation This discussion touched on one of the same issues as the first breakout session: the potential utility of automated prefiltering and preliminary analysis of data.
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That may involve developing visualization methods to ease the job of the analysts or simply developing a comprehensive, searchable database. Visualization itself can play many roles, from helping to identify anomalous events to simply presenting data in a form that lets analysts and decision-makers interpret information faster or focus their attention on particularly interesting portions or aspects of the data.
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The value of developing robust methods for combining quantitative and qualitative data in the study of IED activities was repeatedly raised during the discussion, and participants wished to develop metrics for analysis for both types of data. The goal of data fusion in this context is to assist in the prediction, identification, and ideally, prevention of IED activities.
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, and be adaptive to remain relevant. Data fusion may also require synthesis between datasets of different sizes, such as cellular-telephone records and suspect interviews.
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Collection, Handling, and Preprocessing of Data Another common theme in the workshop discussions was the collection, complexity, and methods of handling and treating the breadth of data relevant to predicting IED activities. Given the broad variety of data sources that are relevant to predicting IED activities, research on combining structured and unstructured data will be particularly valuable.
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That can lead to incomplete datasets because of the difficulty of collecting data consistently and collecting data with large, highly variable background signals and noise. In addition, data may be acquired in any number of forms -- including audio, video, handwritten notes, and measurements from wireless 46
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However, verification of data acquired in the field, such as data from human intelligence, may be difficult. Basic research in signal processing, data fusion, and system modeling could provide tools for addressing those issues.
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