The Architectural Dynamics of Encapsulated Botnet Detection (EDM)

Botnet is one of the numerous attacks ravaging the networking environment. Its approach is said to be brutal and dangerous to network infrastructures as well as client systems. Since the introduction of botnet, different design methods have been employed to solve the divergent approach but the method of taking over servers and client systems is unabated. To solve this, we first identify Mpack, ICEpack and Fiesta as enhanced IRC tool. The analysis of its role in data exchange using OSI model was carried out. This further gave the needed proposal to the development of a High level architecture representing the structural mechanism and the defensive mechanism within network server so as to control the botnet trend. Finally, the architecture was designed to respond in a proactive state when scanning and synergizing the double data verification modules in an encapsulation manner within server system

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

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

The EU and its Counter-Terrorism Policies after the Paris Attacks. Liberty and Security in Europe No. 84, 27 November 2015

This paper examines the EU’s counter-terrorism policies responding to the Paris attacks of 13 November 2015. It argues that these events call for a re-think of the current information-sharing and preventive-justice model guiding the EU’s counter-terrorism tools, along with security agencies such as Europol and Eurojust. Priority should be given to independently evaluating ‘what has worked’ and ‘what has not’ when it comes to police and criminal justice cooperation in the Union. Current EU counter-terrorism policies face two challenges: one is related to their efficiency and other concerns their legality. ‘More data’ without the necessary human resources, more effective cross-border operational cooperation and more trust between the law enforcement authorities of EU member states is not an efficient policy response. Large-scale surveillance and preventive justice techniques are also incompatible with the legal and judicial standards developed by the Court of Justice of the EU. The EU can bring further added value first, by boosting traditional policing and criminal justice cooperation to fight terrorism; second, by re-directing EU agencies’ competences towards more coordination and support in cross-border operational cooperation and joint investigations, subject to greater accountability checks (Europol and Eurojust +); and third, by improving the use of policy measures following a criminal justice-led cooperation model focused on improving cross-border joint investigations and the use of information that meets the quality standards of ‘evidence’ in criminal judicial proceedings. Any EU and national counter-terrorism policies must not undermine democratic rule of law, fundamental rights or the EU’s founding constitutional principles, such as the free movement of persons and the Schengen system. Otherwise, these policies will defeat their purpose by generating more insecurity, instability, mistrust and legal uncertainty for all

On the Use of Data Mining Techniques for Crime Profiling

Crime is today a salient fact, an integral part of the risks we face in everyday life. The concern about national andinternational security has increased significantly since the incident of September 11th, 2001 attacks. However, informationoverload thwarts the effective and efficient analysis of criminal activities. Application of data mining in the context of lawenforcement and intelligence analysis holds the promise of solving such problems. The benefit of data mining for policeseems tremendous, yet only a few limited applications are documented. Data mining can be used to model crime detectionproblems. Any research that can help in solving crimes faster will pay for itself. This paper gives reviews current trends inprofiling crime using data mining techniques. We proposed the use of clustering algorithm as a data mining approach to helpdetect the crimes patterns and speed up the process of solving crime.Key words: Crime, profiling, data mining, criminals, attacks and detectio

The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

Overcoming Human Trafficking via Operations Research and Analytics: Opportunities for Methods, Models, and Applications

Human trafficking is a transnational complex societal and economic issue. While human trafficking has been studied in a variety of contexts, including criminology, sociological, and clinical domains, to date there has been very little coverage in the operations research (OR) and analytics community. This paper highlights how operations research and analytics techniques can be used to address the growing issue of human trafficking. It is intended to give insight to operations research and analytics professionals into the unique concerns, problems, and challenges in human trafficking; the relevance of OR and analytics to key pillars of human trafficking including prevention, protection, and prosecution; and to discuss opportunities for OR and analytics to make a difference in the human trafficking domain. We maintain that a profound need exists to explore how operations research and analytics can be effectively leveraged to combat human trafficking, and set forth this call to action to inhibit its pervasiveness