Repensar o conceito de prevenção do crime na esfera aduaneira

There is always a tendency in law that there is no way a society can exist without a crime commission, as a society without crime is like a human being without blood. Even though the crime commission has become a common phenomenon in a given society, its reduction and prevention are very imperative for the interest and well-being of the society. Every society that is regulated by a legitimate setup has the responsibility in ensuring the peace and security of this society by taking relevant measures in reducing the rate of crime committed in that society. The problem we face here remains that even though with all the laudable efforts of the various law enforcement agencies and organs in Ukraine, it is still practically difficult and impossible in preventing crime commissions in the country, and there still exists a continuous increase rate of crimes committed in the country. This constant increase in crime commission has provoked a doubt in the mind of many as the customary objective of the society is that of crime prevention. In answering this critical controversy that has surrounded the state of Ukraine as to problem affecting the prevention of crime, it will be proper for us to examine some of those justifications posed that have made it difficult for the State of Ukraine and other law enforcement officials in combating and preventing crimes in the country. It is therefore this backdrop that we think something needs to be done by the State of Ukraine to use all the appropriate measures in ensuring security and social order in the society.Siempre se tiende a decir que es imposible que exista una sociedad sin comisión de delitos, ya que una sociedad sin delitos es como un ser humano sin sangre. Aunque la comisión de delitos se ha convertido en un fenómeno común en una sociedad determinada, su reducción y prevención son muy imperativas para el interés y el bienestar de la sociedad. Toda sociedad regulada por un sistema legítimo tiene la responsabilidad de garantizar la paz y la seguridad de esta sociedad adoptando las medidas pertinentes para reducir la tasa de delitos cometidos en esa sociedad. El problema al que nos enfrentamos es que, a pesar de todos los esfuerzos loables de los diversos organismos y órganos encargados de hacer cumplir la ley en Ucrania, sigue siendo prácticamente difícil e imposible prevenir la comisión de delitos en el país, y sigue existiendo una tasa de aumento continuo de los delitos cometidos en el país. Este aumento constante de la comisión de delitos ha provocado una duda en la mente de muchos, ya que el objetivo habitual de la sociedad es el de la prevención del delito. Para responder a esta controversia crítica que ha rodeado al Estado de Ucrania en cuanto al problema que afecta a la prevención de la delincuencia, será conveniente que examinemos algunas de las justificaciones planteadas que han dificultado al Estado de Ucrania y a otros funcionarios encargados de hacer cumplir la ley la lucha y la prevención de los delitos en el país. Por lo tanto, creemos que el Estado de Ucrania debe hacer algo para utilizar todas las medidas apropiadas para garantizar la seguridad y el orden social en la sociedad.Há sempre uma tendência na lei de que não há como uma sociedade existir sem uma comissão de crime, pois uma sociedade sem crime é como um ser humano sem sangue. Mesmo que a comissão de crime tenha se tornado um fenômeno comum em uma dada sociedade, sua redução e prevenção são muito imperativas para o interesse e o bem-estar da sociedade. Toda sociedade que é regulada por um esquema legítimo tem a responsabilidade de garantir a paz e a segurança desta sociedade, tomando medidas relevantes para reduzir a taxa de crimes cometidos nesta sociedade. O problema que enfrentamos aqui continua sendo que, mesmo com todos os esforços louváveis das diversas agências e órgãos de aplicação da lei na Ucrânia, ainda é praticamente difícil e impossível prevenir as comissões de crimes no país, e ainda existe um aumento contínuo da taxa de crimes cometidos no país. Este aumento constante das comissões de crimes tem provocado uma dúvida na mente de muitos, pois o objetivo habitual da sociedade é o da prevenção do crime. Ao responder a esta controvérsia crítica que tem cercado o estado da Ucrânia quanto aos problemas que afetam a prevenção do crime, será conveniente examinarmos algumas das justificativas apresentadas que dificultaram ao Estado da Ucrânia e a outros oficiais da lei o combate e a prevenção dos crimes no país. Portanto, é neste contexto que pensamos que algo precisa ser feito pelo Estado da Ucrânia para utilizar todas as medidas apropriadas para garantir a segurança e a ordem social na sociedade

Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today\u27s cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human interpretation, explainability, and trustworthiness in decision-making, is an essential factor, particularly in cybersecurity application areas. This article provides an in-depth study on multi-aspect rule based AI modeling considering human interpretable decisions as well as security automation and intelligence for CI. We also provide a taxonomy of rule generation methods by taking into account not only knowledge-driven approaches based on human expertise but also data-driven approaches, i.e., extracting insights or useful knowledge from data, and their hybridization. This understanding can help security analysts and professionals comprehend how systems work, identify potential threats and anomalies, and make better decisions in various real-world application areas. We also cover how these techniques can address diverse cybersecurity concerns such as threat detection, mitigation, prediction, diagnosis for root cause findings, and so on in different CI sectors, such as energy, defence, transport, health, water, agriculture, etc. We conclude this paper with a list of identified issues and opportunities for future research, as well as their potential solution directions for how researchers and professionals might tackle future generation cybersecurity modeling in this emerging area of study

From Simple to Sophisticated: The Organization of Terrorist Groups

This dissertation draws on gang organization research and organizational theory to assess the underlying dimensions of organization in terrorist groups. Using the Leadership for the Extreme and Dangerous for Innovative Results (LEADIR) dataset, findings suggest that organization is a multidimensional construct in terrorist groups, including the structuring of activities dimension and the concentration of authority dimension. In relation to violence, terrorist groups high on the structuring of activities dimension were significantly more lethal in general and more lethal when attacking hard targets, whereas terrorist groups high on the concentration of authority dimension attacked hard targets at a significantly higher rate. These findings demonstrate that both dimensions of organization were related to an increased capacity for violence yet in different ways. In light of these findings, a theoretical model is outlined, and practical implications are discussed with a focus on how both organizational dimensions highlight the role of criminal capital and bureaucratic control mechanisms in terrorist groups

Security Enhanced Applications for Information Systems

Every day, more users access services and electronically transmit information which is usually disseminated over insecure networks and processed by websites and databases, which lack proper security protection mechanisms and tools. This may have an impact on both the users’ trust as well as the reputation of the system’s stakeholders. Designing and implementing security enhanced systems is of vital importance. Therefore, this book aims to present a number of innovative security enhanced applications. It is titled “Security Enhanced Applications for Information Systems” and includes 11 chapters. This book is a quality guide for teaching purposes as well as for young researchers since it presents leading innovative contributions on security enhanced applications on various Information Systems. It involves cases based on the standalone, network and Cloud environments

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

A graph oriented approach for network forensic analysis

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex multi-stage intrusions. This dissertation presents a novel graph based network forensic analysis system. The evidence graph model provides an intuitive representation of collected evidence as well as the foundation for forensic analysis. Based on the evidence graph, we develop a set of analysis components in a hierarchical reasoning framework. Local reasoning utilizes fuzzy inference to infer the functional states of an host level entity from its local observations. Global reasoning performs graph structure analysis to identify the set of highly correlated hosts that belong to the coordinated attack scenario. In global reasoning, we apply spectral clustering and Pagerank methods for generic and targeted investigation respectively. An interactive hypothesis testing procedure is developed to identify hidden attackers from non-explicit-malicious evidence. Finally, we introduce the notion of target-oriented effective event sequence (TOEES) to semantically reconstruct stealthy attack scenarios with less dependency on ad-hoc expert knowledge. Well established computation methods used in our approach provide the scalability needed to perform post-incident analysis in large networks. We evaluate the techniques with a number of intrusion detection datasets and the experiment results show that our approach is effective in identifying complex multi-stage attacks

Hidden in Plain Sight: A Machine Learning Approach for Detecting Prostitution Activity in Phoenix, Arizona

Prostitution has been a topic of study for decades, yet many questions remain about where prostitution occurs. Difficulty in identifying prostitution activity is often attributed to the hidden and seemingly victimless nature of the crime. Despite numerous challenges associated with policing street prostitution, these encounters become more difficult to identify when they take place indoors, especially in locations away from public view, such as hotels. The purpose of this paper is to develop a strategy for identifying hotel facilities and surrounding areas that may be experiencing elevated levels of prostitution activity using high-volume, user-generated data, namely hotel reviews written by guests and posted to Travelocity.com. A unique synthesis of methods including data mining, natural language processing, machine learning, and basic spatial analysis are combined to identify facilities that may require additional law enforcement resources and/or social/health service outreach. Prostitution hotspots are identified within the city of Phoenix, Arizona and policy implications are discussed

Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

Ubiquitous intelligence for smart cities: a public safety approach

Citizen-centered safety enhancement is an integral component of public safety and a top priority for decision makers in a smart city development. However, public safety agencies are constantly faced with the challenge of deterring crime. While most smart city initiatives have placed emphasis on the use of modern technology for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in a resource constrained environment, such as in Africa. In particular, crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations and has great potential in helping to fight against crime and promoting safety in smart cities. This research focuses on detecting the situation of crime through data mining approaches that can be used to promote citizens' safety, and assist security agencies in knowledge-driven decision support, such as crime series identification. While much research has been conducted on crime hotspots, not enough has been done in the area of identifying crime series. This thesis presents a novel crime clustering model, CriClust, for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset, drawing on sound scientific and mathematical principles, as well as assumptions from theories of environmental criminology. The analysis is augmented using a dual-threshold model, and pattern prevalence information is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger-Stein mincut algorithm. We introduce two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals underlying strong correlations and defining features for a series. Our findings on experimental quasi-real data set, generated based on expert knowledge recommendation, reveal that identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery in a smart city development. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to competing baseline algorithms and related research. It was found that Monte Carlo technique and adaptive graph size mechanism for crime similarity clustering yield substantial improvement. The study also found that proportion estimation (PDE) and PSE of series clusters can provide valuable insight into crime deterrence strategies. Furthermore, visual enhancement of clusters using graphical approaches to organising information and presenting a unified viable view promotes a prompt identification of important areas demanding attention. Our model particularly attempts to preserve desirable and robust statistical properties. This research presents considerable empirical evidence that the proposed crime cluster (CriClust) model is promising and can assist in deriving useful crime pattern knowledge, contributing knowledge services for public safety authorities and intelligence gathering organisations in developing nations, thereby promoting a sustainable "safe and smart" city