SMEs, electronically-mediated working and data security: cause for concern?

Security of data is critical to the operations of firms. Without the ability to store, process and transmit data securely, operations may be compromised, with the potential for serious consequences to trading integrity. Thus the role that electronically-mediated working plays in business today and its dependency on data security is of critical interest, especially in light of the fact that much of this communication is based on the use of open networks (i.e. the Internet). This paper discusses findings from a 'WestFocus' survey on electronically-mediated working and telework amongst a sample of SMEs located in West London and adjacent counties in South-Eastern England in order to highlight the problems that such practice raises in terms of data security. Data collection involved a telephone survey undertaken in early 2006 of 378 firms classified into four industrial sectors ('Media', 'Logistics', 'Internet Services' and 'Food Processing'). After establishing how ICTs and the Internet are being exploited as business applications for small firms, data security practice is explored on the basis of sector and size with a focus on telework. The paper goes on to highlight areas of concern in terms of data security policy and training practice. Findings show some sector and size influences.WestFocus* under the Higher Education Innovation Fund (HEIF 2

Achieving Energy Efficiency on Networking Systems with Optimization Algorithms and Compressed Data Structures

To cope with the increasing quantity, capacity and energy consumption of transmission and routing equipment in the Internet, energy efficiency of communication networks has attracted more and more attention from researchers around the world. In this dissertation, we proposed three methodologies to achieve energy efficiency on networking devices: the NP-complete problems and heuristics, the compressed data structures, and the combination of the first two methods. We first consider the problem of achieving energy efficiency in Data Center Networks (DCN). We generalize the energy efficiency networking problem in data centers as optimal flow assignment problems, which is NP-complete, and then propose a heuristic called CARPO, a correlation-aware power optimization algorithm, that dynamically consolidate traffic flows onto a small set of links and switches in a DCN and then shut down unused network devices for power savings. We then achieve energy efficiency on Internet routers by using the compressive data structure. A novel data structure called the Probabilistic Bloom Filter (PBF), which extends the classical bloom filter into the probabilistic direction, so that it can effectively identify heavy hitters with a small memory foot print to reduce energy consumption of network measurement. To achieve energy efficiency on Wireless Sensor Networks (WSN), we developed one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. Based on the Open Vehicle Routing problem, EDAL exploits the topology requirements of Compressive Sensing (CS), then implement CS to save more energy on sensor nodes

Complex Methods Applied to Data Analysis, Processing, and Visualisation

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio)

Statistical Foundations of Actuarial Learning and its Applications

This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus

The economic impact of cybercrime and cyber espionage

Introduction Is cybercrime, cyber espionage, and other malicious cyber activities what some call “the greatest transfer of wealth in human history,” or is it what others say is a “rounding error in a fourteen trillion dollar economy?” The wide range of existing estimates of the annual loss—from a few billion dollars to hundreds of billions—reflects several difficulties. Companies conceal their losses and some are not aware of what has been taken. Intellectual property is hard to value. Some estimates relied on surveys, which provide very imprecise results unless carefully constructed. One common problem with cybersecurity surveys is that those who answer the questions “self-select,” introducing a possible source of distortion into the results. Given the data collection problems, loss estimates are based on assumptions about scale and effect— change the assumption and you get very different results. These problems leave many estimates open to question. The Components of Malicious Cyber Activity In this initial report we start by asking what we should count in estimating losses from cybercrime and cyber espionage. We can break malicious cyber activity into six parts: The loss of intellectual property and business confidential information Cybercrime, which costs the world hundreds of millions of dollars every year The loss of sensitive business information, including possible stock market manipulation Opportunity costs, including service and employment disruptions, and reduced trust for online activities The additional cost of securing networks, insurance, and recovery from cyber attacks Reputational damage to the hacked company Put these together and the cost of cybercrime and cyber espionage to the global economy is probably measured in the hundreds of billions of dollars. To put this in perspective, the World Bank says that global GDP was about $70 trillion in 2011. A$400 billion loss—the high end of the range of probable costs—would be a fraction of a percent of global income. But this begs several important questions about the full benefit to the acquirers and the damage to the victims from the cumulative effect of cybercrime and cyber espionage