The Experimental Analysis of Information Security Management Issues for Online Financial Services

E-commerce offers the banking industry great opportunity, but also creates a set of new risks and vulnerability such as security threats. Information security, therefore, is an essential management and technical requirement for any efficient and effective financial activities over the Internet. Still, its definition is a complex endeavor due to the constant technological and business change and requires a coordinated match of managerial and technical solutions. This research intends to provide an instrument to test and evaluate the strategies to counter threats facing online financial institutions through an artificial economic setup at the Synthetic Environments for Simulation and Analysis (SEAS) laboratory at the Krannert Graduate School of Management, Purdue University. The research also intends to provide guidelines for forming information security policies and strategies for survival and success in the dynamic and hostile business environment. Initial results indicate that online banks that were proactive in recognizing the threats and devising policies to counter them generated greater revenue and were able to focus on the core activities. Public disclosure of security breaches by the victim banks resulted in better overall health of the simulated economy. The simulation is still in its development and testing phase and the research team intends to present the findings at the conference

DATUM in Action

This collaborative research data management planning project (hereafter the RDMP project) sought to help a collaborative group of researchers working on an EU FP7 staff exchange project (hereafter the EU project) to define and implement good research data management practice by developing an appropriate DMP and supporting systems and evaluating their initial implementation. The aim was to "improve practice on the ground" through more effective and appropriate systems, tools/solutions and guidance in managing research data. The EU project (MATSIQEL - (Models for Ageing and Technological Solutions For Improving and Enhancing the Quality of Life), funded under the Marie Curie International Research Staff Exchange Scheme, is accumulating expertise for the mathematical and computer modelling of ageing processes with the aim of developing models which can be implemented in technological solutions (e.g. monitors, telecare, recreational games) for improving and enhancing quality of life.1 Marie Curie projects do not fund research per se, so the EU project has no resources to fund commercial tools for research data management. Lead by Professor Maia Angelova, School of Computing, Engineering and Information Sciences (SCEIS) at Northumbria University, it comprises six work packages involving researchers at Northumbria and in Australia, Bulgaria, Germany, Mexico and South Africa. The RDMP project focused on one of its work packages (WP4 Technological Solutions and Implementation) with some reference to another work package lead by the same person at Northumbria University (WP5 Quality of Life). The RDMP project‟s innovation was less about the choice of platform/system, as it began with existing standard office technology, and more about how this can be effectively deployed in a collaborative scenario to provide a fit-for-purpose solution with useful and usable support and guidance. It built on the success of the Datum for Health project by taking it a stage further, moving from a solely health discipline to an interdisciplinary context of health, social care and mathematical/computer modelling, and from a Postgraduate Research Student context to an academic researcher context, with potential to reach beyond the University boundaries. In addition, since the EU project is re-using data from elsewhere as well as creating its own data; a wide range of RDM issues were addressed. The RDMP project assessed the transferability of the DATUM materials and the tailored DATUM DMP

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.Comment: 38 pages, 19 figures

Co-designing climate-smart farming systems with local stakeholders: A methodological framework for achieving large-scale change

The literature is increasing on how to prioritize climate-smart options with stakeholders but relatively few examples exist on how to co-design climate-smart farming systems with them, in particular with smallholder farmers. This article presents a methodological framework to co-design climate-smart farming systems with local stakeholders (farmers, scientists, NGOs) so that large-scale change can be achieved. This framework is based on the lessons learned during a research project conducted in Honduras and Colombia from 2015 to 2017. Seven phases are suggested to engage a process of co-conception of climate-smart farming systems that might enable implementation at scale: (1) “exploration of the initial situation,” which identifies local stakeholders potentially interested in being involved in the process, existing farming systems, and specific constraints to the implementation of climate-smart agriculture (CSA); (2) “co-definition of an innovation platform,” which defines the structure and the rules of functioning for a platform favoring the involvement of local stakeholders in the process; (3) “shared diagnosis,” which defines the main challenges to be solved by the innovation platform; (4) “identification and ex ante assessment of new farming systems,” which assess the potential performances of solutions prioritized by the members of the innovation platform under CSA pillars; (5) “experimentation,” which tests the prioritized solutions on-farm; (6) “assessment of the co-design process of climate-smart farming systems,” which validates the ability of the process to reach its initial objectives, particularly in terms of new farming systems but also in terms of capacity building; and (7) “definition of strategies for scaling up/out,” which addresses the scaling of the co-design process. For each phase, specific tools or methodologies are used: focus groups, social network analysis, theory of change, life-cycle assessment, and on-farm experiments. Each phase is illustrated with results obtained in Colombia or Honduras