Early Identification of Violent Criminal Gang Members

Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.Comment: SIGKDD 201

What are the roles of the Internet in terrorism? Measuring online behaviours of convicted UK terrorists

Using a unique dataset of 227 convicted UK-based terrorists, this report fills a large gap in the existing literature. Using descriptive statistics, we first outline the degree to which various online activities related to radicalisation were present within the sample. The results illustrate the variance in behaviours often attributed to ‘online radicalisation’. Second, we conducted a smallest-space analysis to illustrate two clusters of commonly co-occurring behaviours that delineate behaviours from those directly associated with attack planning. Third, we conduct a series of bivariate and multivariate analyses to question whether those who interact virtually with like-minded individuals or learn online, exhibit markedly different experiences (e.g. radicalisation, event preparation, attack outcomes) than those who do not

Indicadores reticulares para la detección de abonados telefónicos potencialmente relevantes en el marco de investigaciones judiciales

En el marco de las investigaciones judiciales, resulta habitual la utilización de datos de telecomunicaciones como fuente de información preliminar y/o probatoria sobre grupos de criminalidad compleja. Los agentes judiciales, en gran medida, están acostumbrados a la recolección de datos telefónicos, pero no así a su procesamiento y análisis. Esta situación se agrava más día a día con la explosión de grandes volúmenes de información. Para dar respuesta a esta dificultad, y dentro de una dependencia judicial abocada al desarrollo de pericias interdisciplinarias para el apoyo a la investigación penal, se conformó un equipo al cual pertenezco que realiza procedimientos de preparación y consolidación de la información a través de bases de datos relacionales y los analiza posteriormente mediante el análisis de redes sociales (ARS). Dado que una de las características específicas de las redes criminales está dada por el hecho que los actores dentro de un grupo criminal procuran no dejar rastros de sus interacciones, la utilización de indicadores reticulares provee de trazabilidad, eficiencia y rigurosidad, todos ellos valores que no se pueden obviar por ser relevantes en el proceso penal. En ese sentido, los algoritmos de centralidad parecen vincularse de forma específica con determinados roles dentro de una organización criminal. La centralidad de grado es el indicador más intuitivo y se correlaciona con aquellos actores más visibles para el accionar judicial. La centralidad de intermediación, al ser una medida dependiente de la estructura global de la red, resulta ya más difícil de elucidar para el operador judicial, brindando información de potencial interés. Por último, la conjunción de ambos brinda el índice de centralidad combinada cuyo objetivo es detectar “jugadores claves”, es decir, aquellos nodos con pocos vínculos, pero de mayor calidad, arrojando los resultados más interesantes por contraintuitivos y, por ende, la más provechosa fuente de información para la investigación penal.GT69. Antropología aplicada y modelos complejos: expandiendo la frontera metodológica.Universidad Nacional de La Plat

Data Driven Inference in Populations of Agents

abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.   This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Automatic Identification of Online Predators in Chat Logs by Anomaly Detection and Deep Learning

Providing a safe environment for juveniles and children in online social networks is considered as a major factor in improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of juvenile abuse in cyberspace has become inevitable. Using automatic ways to address this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and binary classification in machine learning. This thesis proposes two machine learning approaches to deal with the following two issues in the domain of online predator identification: 1) The first problem is gathering a comprehensive set of negative training samples which is unrealistic due to the nature of the problem. This problem is addressed by applying an existing method for semi-supervised anomaly detection that allows the training process based on only one class label. The method was tested on two datasets; 2) The second issue is improving the performance of current binary classification methods in terms of classification accuracy and F1-score. In this regard, we have customized a deep learning approach called Convolutional Neural Network to be used in this domain. Using this approach, we show that the classification performance (F1-score) is improved by almost 1.7% compared to the classification method (Support Vector Machine). Two different datasets were used in the empirical experiments: PAN-2012 and SQ (Sûreté du Québec). The former is a large public dataset that has been used extensively in the literature and the latter is a small dataset collected from the Sûreté du Québec

Redes criminosas em investigações previdenciárias na Polícia Federal

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas, Programa de Pós-Graduação em Administração, Mestrado Profissional em Administração Pública, 2020.As fraudes contra o sistema previdenciário brasileiro causam prejuízo bilionário anualmente aos cofres públicos e o presente trabalho busca formas de auxiliar à Polícia Federal no combate a este tipo de fraude. Para tanto foram estudados, com o método da Análise de Redes Sociais, alguns casos já concluídos de operações de combate a grupos criminosos atuantes naquela área, delineando uma rede básica de composição de grupos criminosos e propondo algoritmo de detecção ou predição destes grupos. O algoritmo foi testado em investigações em andamento em quatro unidades da federação, identificando a existência de 37 prováveis grupos criminosos organizados envolvidos em fraudes apuradas em diversas investigações feitas de maneira isolada. O algoritmo proposto pode auxiliar não só na identificação ou predição de grupos criminosos, mas também ajudar na definição, mediante critérios objetivos e com base em método científico, da aplicação dos recursos disponíveis de forma a maximizar os resultados no combate à atuação de grupos criminosos organizados.Frauds against the Brazilian social security system cause billionaire losses annually to public coffers and the present work seeks ways to assist the Federal Police in combating this type of fraud. For this purpose, some cases already concluded of operations to combat criminal groups operating in that area were studied, using the Social Network Analysis method, outlining a basic network of composition of criminal groups and proposing an algorithm for detecting or predicting these groups. The algorithm was tested in ongoing investigations in four units of the federation, identifying the existence of 37 probable organized criminal groups involved in fraud investigated in several investigations carried out in isolation. The proposed algorithm can assist not only in the identification or prediction of criminal groups, but also in the definition, using objective criteria and based on a scientific method, of the application of available resources in order to maximize the results in combating the activities of organized criminal groups

Data mining no contra-terrorismo: uma abordagem para a compreensão dos factores determinantes do terrorismo

O trabalho apresentado procurou constituir uma abordagem ao estudo do terrorismo no sentido de estabelecer mais um passo para a compreensão dos processos ou indicadores subjacentes à radicalização nas suas formas mais extremas. Definiram-se como objectivos a identificação dos factores determinantes do terrorismo e a concepção de um modelo explicativo e preditivo para a ocorrência e impacto de actos terroristas. Entraram em análise como potenciais factores explicativos variáveis das dimensões económica, sociodemográfica, política, religiosa e de carácter multidimensional, integradas no âmbito de uma amostra de 173 países, organizada segundo modelo de dados em painel, por um período de 20 anos decorridos entre 1990 e 2010. Através da aplicação de Árvores de Regressão com o algoritmo CART, uma das várias técnicas de data mining que são particularmente adequadas quando se têm muitos dados de origem multidimensional, foram identificados diversos factores considerados como determinantes do terrorismo, pertencentes às cinco dimensões de variáveis, tendo-se confirmado a hipótese que sustenta que os factores sociodemográficos são os que melhor explicam a actividade terrorista. Os modelos explicativos para os três indicadores de terrorismo não traduziram capacidades explicativas e preditivas muito elevadas, mas dada a complexidade e subjectividade que envolve o fenómeno do terrorismo, considera-se que os modelos gerados constituem mais um contributo para a literatura sobre terrorismo. Não tendo sido identificado nenhum estudo anterior em que esta técnica de análise tenha sido utilizada, pôde-se confirmar a sua eficácia para a concretização deste e de futuros estudos na área do terrorismo.The presented study aimed to provide an approach to the study of terrorism in order to establish a further step towards the understanding of the processes or underlying indicators of radicalization in its most extreme forms. The main goals were the identification of the determinants of terrorism and the conception of an explanatory and predictive model for the occurrence and impact of terrorist acts. Under analysis were, as potential explanatory factors, economic, sociodemographic, political, religious and multidimensional variables, integrated within a sample of 173 countries, organized according to panel data model, for a period of 20 years between 1990 and 2010. Through the application of Regression Trees with the CART algorithm, one of several data mining techniques that are particularly suitable to the analysis of many multidimensional data sources, there were identified several factors considered as determinants of terrorism, belonging to the five variable dimensions, having been confirmed the hypothesis that holds that sociodemographic factors are those that best explain terrorist activity. The explanatory models for the three indicators of terrorism did not hold explanatory and predictive capacities very high, but given the complexity and subjectivity involved in the phenomenon of terrorism, it is considered that the generated models are a further contribution to the literature on terrorism. Having not been identified any previous studies in which this analysis technique has been used, it is therefore possible to confirm its efficiency to achieve this and future studies in the area of terrorism