Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

Introduction to Multi-Agent Simulation

When designing systems that are complex, dynamic and stochastic in nature, simulation is generally recognised as one of the best design support technologies, and a valuable aid in the strategic and tactical decision making process. A simulation model consists of a set of rules that define how a system changes over time, given its current state. Unlike analytical models, a simulation model is not solved but is run and the changes of system states can be observed at any point in time. This provides an insight into system dynamics rather than just predicting the output of a system based on specific inputs. Simulation is not a decision making tool but a decision support tool, allowing better informed decisions to be made. Due to the complexity of the real world, a simulation model can only be an approximation of the target system. The essence of the art of simulation modelling is abstraction and simplification. Only those characteristics that are important for the study and analysis of the target system should be included in the simulation model. The purpose of simulation is either to better understand the operation of a target system, or to make predictions about a target system’s performance. It can be viewed as an artificial white-room which allows one to gain insight but also to test new theories and practices without disrupting the daily routine of the focal organisation. What you can expect to gain from a simulation study is very well summarised by FIRMA (2000). His idea is that if the theory that has been framed about the target system holds, and if this theory has been adequately translated into a computer model this would allow you to answer some of the following questions: · Which kind of behaviour can be expected under arbitrarily given parameter combinations and initial conditions? · Which kind of behaviour will a given target system display in the future? · Which state will the target system reach in the future? The required accuracy of the simulation model very much depends on the type of question one is trying to answer. In order to be able to respond to the first question the simulation model needs to be an explanatory model. This requires less data accuracy. In comparison, the simulation model required to answer the latter two questions has to be predictive in nature and therefore needs highly accurate input data to achieve credible outputs. These predictions involve showing trends, rather than giving precise and absolute predictions of the target system performance. The numerical results of a simulation experiment on their own are most often not very useful and need to be rigorously analysed with statistical methods. These results then need to be considered in the context of the real system and interpreted in a qualitative way to make meaningful recommendations or compile best practice guidelines. One needs a good working knowledge about the behaviour of the real system to be able to fully exploit the understanding gained from simulation experiments. The goal of this chapter is to brace the newcomer to the topic of what we think is a valuable asset to the toolset of analysts and decision makers. We will give you a summary of information we have gathered from the literature and of the experiences that we have made first hand during the last five years, whilst obtaining a better understanding of this exciting technology. We hope that this will help you to avoid some pitfalls that we have unwittingly encountered. Section 2 is an introduction to the different types of simulation used in Operational Research and Management Science with a clear focus on agent-based simulation. In Section 3 we outline the theoretical background of multi-agent systems and their elements to prepare you for Section 4 where we discuss how to develop a multi-agent simulation model. Section 5 outlines a simple example of a multi-agent system. Section 6 provides a collection of resources for further studies and finally in Section 7 we will conclude the chapter with a short summary.

Carbon losses in terrestrial hydrological pathways in sugarcane cropping systems of Australia

Climate change and carbon (C) sequestration are a major focus of research in the twenty-first century. Globally, soils store about 300 times the amount of C that is released per annum through the burning of fossil fuels (Schulze and Freibauer 2005). Land clearing and introduction of agricultural systems have led to rapid declines in soil C reserves. The recent introduction of conservation agricultural practices has not led to a reversing of the decline in soil C content, although it has minimized the rate of decline (Baker et al. 2007; Hulugalle and Scott 2008). Lal (2003) estimated the quantum of C pools in the atmosphere, terrestrial ecosystems, and oceans and reported a “missing C” component in the world C budget. Though not proven yet, this could be linked to C losses through runoff and soil erosion (Lal 2005) and a lack of C accounting in inland water bodies (Cole et al. 2007). Land management practices to minimize the microbial respiration and soil organic C (SOC) decline such as minimum tillage or no tillage were extensively studied in the past, and the soil erosion and runoff studies monitoring those management systems focused on other nutrients such as nitrogen (N) and phosphorus (P).

Collaborative Systems of Systems Need Collaborative Design

Part 1: Introduction

Collaborative Systems for Smart Environments: Trends and Challenges

Part 1: Introduction

Standardized environmental management systems as an internal management tool

In a principal-agent model we analyze the firm’s decision to adopt an informal or a standardized Environmental Management System (EMS). Our results are consistent with empirical evidence in several respects. A standardized EMS increases the internal control at the cost of introducing some degree of rigidity that entails an endogenous setup cost. Standardized systems are more prone to be adopted by big and well established firms and under tougher environmental policies. Firms with standardized EMS tend to devote more effort to abatement although this effort results in lower pollution only if public incentives are strong enough, suggesting a complementarity relationship between standardized EMS and public policies. Emission charges have both a marginal effect on abatement and a qualitative effect on the adoption decision that may induce a conflict between private and public interests. As a result of the combination of these two effects it can be optimal for the government to distort the tax in a specific way in order to push the firm to choose the socially optimal EMS. The introduction of standardized systems can result in win-win situations where firms, society and the environment get better off.

Introduction

As liquid media at a temperature less than 100 °C that possess some level of ionic conductivity, it is immediately of interest to consider the use of ionic liquids (ILs) as electrolytes to carry out electrochemical processes. This has of course the origins of the modern era of interest in ionic liquids via the work of Wilkes and coworkers in the 1990s [1]. Applications in electrowinning and electrodeposition have developed including processes for a range of metals from copper and zinc to lithium and aluminium [2]. Some metals such as titanium remain, however, stubbornly difficult to electrodeposit [3]. A range of applications in electrochemical devices, including batteries, fuel cells, and solar cells have also emerged and are being thoroughly discussed in Volume 2 (Electrochemistry in ionic liquids. Applications). In parallel to this, there has emerged the need to understand more in detail some important fundamental concepts of electrochemistry as well as the interest on fundamental electrochemical process taking place in an ionic liquid medium and in identifying the ways in which the processes differ, or not, from conventional solvent systems as a result of the highly charged medium [4–6]. Thereby, in this book, special emphasis is placed on showing which aspects of electrochemistry in ionic liquids are different from electrochemistry in conventional solutions. Furthermore, new electrochemical concepts and theories are presented. The book commences with a deep and comprehensive discussion on electrode/electrolyte interface reactions, interface structure, and its critical properties for all electrochemical applications. Chapter 2 discusses these fundamental concepts along with some in situ techniques, such as electrochemical impedance and Fourier transform infrared spectroscopy, cyclic voltammetry, and electrochemical quartz crystal microbalance, suitable for the characterization of electrode/IL interfaces.

Spatial and Functional Organization of Pig Trade in Different European Production Systems: Implications for Disease Prevention and Control

Understanding the complexity of live pig trade organization is a key factor to predict and control major infectious diseases, such as classical swine fever (CSF) or African swine fever (ASF). Whereas the organization of pig trade has been described in several European countries with indoor commercial production systems, little information is available on this organization in other systems, such as outdoor or small-scale systems. The objective of this study was to describe and compare the spatial and functional organization of live pig trade in different European countries and different production systems. Data on premise characteristics and pig movements between premises were collected during 2011 from Bulgaria, France, Italy, and Spain, which swine industry is representative of most of the production systems in Europe (i.e., commercial vs. small-scale and outdoor vs. indoor). Trade communities were identified in each country using the Walktrap algorithm. Several descriptive and network metrics were generated at country and community levels. Pig trade organization showed heterogeneous spatial and functional organization. Trade communities mostly composed of indoor commercial premises were identified in western France, northern Italy, northern Spain, and north-western Bulgaria. They covered large distances, overlapped in space, demonstrated both scale-free and small-world properties, with a role of trade operators and multipliers as key premises. Trade communities involving outdoor commercial premises were identified in western Spain, south-western and central France. They were more spatially clustered, demonstrated scale-free properties, with multipliers as key premises. Small-scale communities involved the majority of premises in Bulgaria and in central and Southern Italy. They were spatially clustered and had scale-free properties, with key premises usually being commercial production premises. These results indicate that a disease might spread very differently according to the production system and that key premises could be targeted to more cost-effectively control diseases. This study provides useful epidemiological information and parameters that could be used to design risk-based surveillance strategies or to more accurately model the risk of introduction or spread of devastating swine diseases, such as ASF, CSF, or foot-and-mouth disease.

Advances in integration of photovoltaic power and energy production in practical systems

This thesis presents advances in integration of photovoltaic (PV) power and energy in practical systems, such as existing power plants in buildings or directly integrated in the public electrical grid. It starts by providing an analyze of the current state of PV power and some of its limitations. The work done in this thesis begins by providing a model to compute mutual shading in large PV plants, and after provides a study of the integration of a PV plant in a biogas power plant. The remainder sections focus on the work done for project PVCROPS, which consisted on the construction and operation of two prototypes composed of a PV system and a novel battery connected to a building and to the public electrical grid. These prototypes were then used to test energy management strategies and validate the suitability of the two advanced batteries (a lithium-ion battery and a vanadium redox ow battery) for households (BIPV) and PV plants. This thesis is divided in 7 chapters: Chapter 1 provides an introduction to explain and develop the main research questions studied for this thesis; Chapter 2 presents the development of a ray-tracing model to compute shading in large PV elds (with or without trackers); Chapter 3 shows the simulation of hybridizing a biogas plant with a PV plant, using biogas as energy storage; Chapters 4 and 5 present the construction, programming, and initial operation of both prototypes (Chapter 4), EMS testing oriented to BIPV systems (Chapter 5). Finally, Chapters 6 provides some future lines of investigation that can follow this thesis, and Chapter 7 shows a synopsis of the main conclusions of this work; Resumo: Avanços na integracão de potência fotovoltaica e producão de energia em sistemas práticos Esta tese apresenta avanços na integração de potência e energia fotovoltaica (PV) em sistemas práticos, tais como centrais existentes ou a rede eléctrica pública. Come ça por analisar o estado corrente do fotovoltaico no mundo e aborda algumas das suas limitações. O trabalho feito para esta tese de doutoramento começou pelo desenvolvimento de um modelo para calcular os sombreamentos que ocorrem em grandes campos fotovoltaicos, e depois apresenta um estudo sobre a integração um sistema fotovoltaico em uma central eléctrica a bióg as. As ultimas secções da tese focam-se no trabalho feito para o projecto PVCROPS, que consistiu na construção e operação de dois demonstratores, cada um formado por um sistema fotovoltaico e bateria conectados a um edíficio e a rede eléctrica pública. Estes protótipos foram posteriormente utilizados para testar estratégias de gestão de energia (EMS) e para validar a operação de duas baterias avançadas (bateria de Iões de Li tio e bateria de Fluxo Redox de Van adio) e a sua utiliza ção para habitações e centrais PV. A tese está dividida em 7 capitulos: O capitulo 1 apresenta uma introdução para explicar e desenvolver as principais questões que foram investigadas nesta tese; O capitulo 2 mostra o desenvolvimento de um modelo baseado em traçados de raios para calcular sombreamentos mútuos em grandes centrais PV (com e sem seguidores); O capitulo 3 mostra a simulação da hibridização de uma central electrica a biogas com uma central PV, e utilizando o biógas como armazenamento de energia. Os capitulos 4 e 5 apresentam a construção, programação e operação inicial dos dois demonstradores (Capitúlo 4), o teste de EMS orientadas para sistemas PV em habitações (Capítulo 5). Finalmente, o capítulo 6 sugere algumas futuras linhas de investigação que poderão seguir esta tese, e o Capítulo 7 faz uma sinopse das principais conclusões deste trabalho.

Computer-based monitoring platform for renewable energy systems: Four experimental application cases using NI LabVIEW

Sustainability and responsible environmental behaviour constitute a vital premise in the development of the humankind. In fact, during last decades, the global energetic scenario is evolving towards a scheme with increasing relevance of Renewable Energy Sources (RES) like photovoltaic, wind, biomass and hydrogen. Furthermore, hydrogen is an energy carrier which constitutes a mean for long-term energy storage. The integration of hydrogen with local RES contributes to distributed power generation and early introduction of hydrogen economy. Intermittent nature of many of RES, for instance solar and wind sources, impose the development of a management and control strategy to overcome this drawback. This strategy is responsible of providing a reliable, stable and efficient operation of the system. To implement such strategy, a monitoring system is required.The present paper aims to contribute to experimentally validate LabVIEW as valuable tool to develop monitoring platforms in the field of RES-based facilities. To this aim, a set of real systems successfully monitored is exposed.

On the zeros of the Abelian integrals for a class of Liénard systems

A planar polynomial differential system has a finite number of limit cycles. However, finding the upper bound of the number of limit cycles is an open problem for the general nonlinear dynamical systems. In this paper, we investigated a class of Liénard systems of the form x'=y, y'=f(x)+y g(x) with deg f=5 and deg g=4. We proved that the related elliptic integrals of the Liénard systems have at most three zeros including multiple zeros, which implies that the number of limit cycles bifurcated from the periodic orbits of the unperturbed system is less than or equal to 3.