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\"Political discourse on immigration in the United States has largely focused on what is most visible, including border walls and detention centers, while the invisible information systems that undergird immigration enforcement have garnered less attention. Tracking the evolution of various surveillance-related systems since the 1980s, Borderland Circuitry investigates how the deployment of this information infrastructure has shaped immigration enforcement practices. Ana Muñiz illuminates three phenomena that are becoming increasingly intertwined: digital surveillance, immigration control, and gang enforcement. Using ethnography, interviews, and analysis of documents never before seen, Muñiz uncovers how information-sharing partnerships between local police, state and federal law enforcement, and foreign partners collide to create multiple digital borderlands. Diving deep into a select group of information systems, Borderland Circuitry reveals how those with legal and political power deploy the specter of violent cross-border criminals to justify intensive surveillance, detention, brutality, deportation, and the destruction of land for border militarization\"-- Provided by publisher.

Terrorists are continuously learning to utilize the Internet as an accessible and cost-effective information infrastructure. Since a constant manual monitoring of terrorist-generated multilingual web content is not a feasible task, automated Web Intelligence and Web Mining methods are indispensable for efficiently securing the Web against its misuse by terrorists and other dangerous criminals. Web Intelligence and Security contains chapters by the key speakers of the NATO Advanced Research Workshop on Web Intelligence and Security that took place on November 18-20, 2009 in Ein-Bokek, Israel. This Workshop has brought together a multinational group of leading scientists and practitioners interested in exploiting data and text mining techniques for countering terrorist activities on the Web. Most talks were focused on presenting available methods and tools that can alleviate the information overload of intelligence and security experts. The key features of this book include: An up-to-date analysis of the current and future threats of the Internet misuse by terrorists and other malicious elements including cyberterrorism, terror financing and interactive online communication by terrorists and their supporters; Detailed presentation of the state-of-the-art algorithms and tools aimed at detecting and monitoring malicious online activities on the Web; Introduction of novel data mining and text mining methods that can be used to efficiently analyze the massive amounts of multi-lingual Web content; The book's wide audience includes research scientists, graduate students, intelligence analysts, and data / text mining practitioners.

It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of \"off the shelf\" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop \"risk-based deployment strategies,\" that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity.Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting. It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis, which law enforcement and intelligence professionals can tailor to their own unique situation and responsibilities.

Written and edited by leading experts in the field, this timely resource presents a thorough overview of the technical facets of Homeland Security (HS). This practical book offers you expert guidance on sensors and the preprocessing of sensed data, the handling of sensed data with secure and safe procedures, and the design, modeling and simulation of complex HS systems. You learn how to store, encrypt and mine sensitive data. Further, the book shows how data is transmitted and received along wired or wireless networks, operating on electromagnetic channels. Critical topics include embedded wireless sensor networks, tapping the vehicle grid for homeland security, visual detection of humans, mining databases, single-database private information retrieval, the channel model for sensor networks in an urban environment, sensing through walls, and stopping cars and mobiles. This unique, cutting-edge volume includes over 100 illustrations and more than 460 references that support key discussions throughout the book.

Computers can be trained to analyse location information generated by social media users to predict the likely time and place of specific crimes.

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.

Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent relative to the best current techniques. Improved fraud prediction can result in meaningful benefits, such as improving the ability of the SEC to detect fraudulent filings and improving audit firms' client portfolio decisions.

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.

In China, small-scale coal mines, particularly those nearing depletion, are frequently affected by out-of-seam and out-of-scope mining, leading to significant safety hazards and frequent accidents. To overcome the limitations of existing detection methods, which are often time-consuming and inaccurate, we propose a novel approach that integrates Kalman and particle dual filtering techniques. This methodology employs handheld positioning and data acquisition devices, carried by law enforcement personnel during mine inspections. The system incorporates a strapdown inertial navigation unit, enhanced by Kalman filtering and a “zero-speed” correction mechanism, to deliver real-time navigation capabilities. As inspectors navigate the mine and pass through designated Bluetooth beacon zones, the particle filtering model processes the navigation data to correct errors dynamically. The resulting data, which includes the inspectors’ positions and movement trajectories, is preprocessed and transmitted to a centralized ground server. This data is then analyzed using Simultaneous Localization and Mapping (SLAM) algorithms, and cross-validated against officially approved mine maps. This approach enables the precise and efficient identification of out-of-seam and out-of-scope mining activities, a capability validated through experimental trials.