TERRORISM FROM A GLOBAL PERSPECTIVE: INFLUENCE AND NETWORK STRUCTURE ANALYSIS

While terrorism is not new, today’s terrorist threat is different from that of the past. Terrorism has evolved, and terrorist groups today are more structured and better organized. Modern technology enables terrorists to plan and operate worldwide as never before. Through the constant exchange of information between parties, perpetrator groups may influence or be influenced by other perpetrator groups to improve their efficacy. This study moves away from the traditional analysis of terrorist groups and examines terrorist networks from a global perspective. Using network science and our proposed methodology to calculate influence strength, this thesis looks at the extent of influence of one perpetrator group with another based on their activities and locations. We observe that some perpetrator groups, like ISIL and Al–Nusrah Front, have high and increasing influence strength. Some of these perpetrator groups are, from a network science perspective, neighbors. In addition, the community detection algorithm shows that most of the perpetrator groups with high influence strength exist within the same network-defined community. Our proposed influence score metric allows measurement of a node's actual influence score based on the responses of other nodes around it, as compared to existing measures, which determine the node's influential strength by its position in the network. We hope our study provides insights into terrorism and how influence spreads among perpetrators.http://archive.org/details/terrorismfromagl1094560417Outstanding ThesisArmy, SingaporeApproved for public release; distribution is unlimited

Exploiting weaknesses: an approach to counter cartel strategy

The thesis, "Exploiting Weaknesses: An Approach to Counter Cartel Strategy," provided an in-depth case study analysis of Los Zetas transnational criminal network to gain an understanding on its weaknesses and vulnerabilities. The thesis utilized social movement theory to illuminate its mobilizing structure and key essential factors that make Los Zetas vulnerable to disruption. In addition, the study identified Los Zetas' financial support structure to expose its insidious methods. Finally, the thesis utilized social network analysis and geographical information systems to gain an understanding of its organizational networks, deduce possible safe havens, and key terrain of Los Zetas. Ultimately, the employment of the aforementioned theories revealed essential vulnerabilities, which form the essence of a practical disruption policy recommendation against Los Zetas.http://archive.org/details/exploitingweakne1094510682US Army (USA) author

Identifying Human Trafficking Networks in Louisiana by Using Authorship Attribution and Network Modeling

Human trafficking or modern slavery is a problem that has plagued every U.S. state, in both urban and rural areas. During the past decades, online advertisements for sex trafficking have rapidly increased in numbers. The advancement of the Internet and smart phones have made it easier for sex traffickers to contact and recruit their victims and advertise and sell them online. Also, they have made it more difficult for law enforcement to trace the victims and identify the traffickers. Sadly, more than fifty percent of the victims of sex trafficking are children, many of which are exploited through the Internet. The first step for preventing and fighting human trafficking is to identify the traffickers. The primary goal of this study is to identify potential organized sex trafficking networks in Louisiana by analyzing the ads posted online in Louisiana and its five neighboring states. The secondary goal of this study is to examine the possibility of using authorship attribution techniques (in addition to phone numbers and ad IDs) to group together the online advertisements that may have been posted by the same entity. The data used in this study was collected from the website Backpage for a time period of ten months. After cleaning the data set, we were left with 123,436 ads from 47 cities in the specified area. Through the application of network analysis, we found many entities that are potentially such networks, all of which posted a large number of ads with many phone numbers in different cities. Also, we identified the time period that each phone number was used in and the cities and states that each entity posted ads for, which shows how these entities moved around between different cities and states. The four supervised machine learning methods that we used to classify the collected advertisements are Support Vector Machines (SVMs), the Naïve Bayesian classifier, Logistic Regression, and Neural Networks. We calculated 40 accuracy rates, 35 of which were over 90% for classifying any number of ads per entity, as long as each entity (or author) posted more than 10 ads

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum

Analysis of Layered Social Networks

Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations. Techniques applying information regarding multiple dimensions of interpersonal relationships, inferring from them the strengths of interpersonal ties, are explored. A layered network construct is offered that provides new analytic opportunities and insights generally unaccounted for in traditional social network analyses. These provide decision makers improved courses of action designed to impute influence upon an adversarial network, thereby achieving a desired influence, perception, or outcome to one or more actors within the target network. This knowledge may also be used to identify key individuals, relationships, and organizational practices. Subsequently, such analysis may lead to the identification of exploitable weaknesses to either eliminate the network as a whole, cause it to become operationally ineffective, or influence it to directly or indirectly support National Security Strategy