Showing posts with label EDUCATIONS. Show all posts
Sunday, July 25, 2010
This study re-examines John Dewey's philosophy of education, and asks how well it stands up today in view of developments in Continental European philosophy. Do Martin Heidegger's statements on the nature of thinking compel a re-examination of Dewey's view? Does Hans-Georg Gadamer's philosophy of experience advance beyond Dewey's experimental model? How does a Deweyan view of moral or political education look in light of Hannah Arendt's theory of judgment, or Paulo Freires' theory of dialogical education? Part One of this study looks at Dewey's conceptions of experience and thinking in connection with two of the most important figures in twentieth-century phenomenology and hermeneutics: Heidegger and Gadamer.
It also returns to an old distinction in the philosophy of education between progressivism and conservatism, in order to situate and clarify Dewey's position and to frame the argument of this book. Part Two applies this principled framework to the teaching of several disciplines of the human sciences: philosophy, religion, ethics, politics, history, and literature. These are discussed with reference to the writings of Friedrich Nietzsche, John Caputo, Hannah Arendt, Paulo Freire, Michel Foucault, and Paul Ricoeur.
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Monday, September 14, 2009
By : Christian Ullrich, Detlef Seese and Stephan Chalup
Abstract.
This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel functions are considered. Each SVM model is benchmarked against traditional forecasting techniques in order to ascertain its potential value as out-of-sample forecasting and quantitative trading tool. It is found that hyperbolic SVMs perform well in terms of forecasting accuracy and trading results via a simulated strategy. This supports the idea that SVMs are promising learning systems for coping with nonlinear classification tasks in the field of financial time series applications.
Introduction
Support Vector Machines (SVMs) have proven to be a principled and very powerful supervised learning system that since its introduction (Cortes and Vapnik (1995)) has outperformed many systems in a variety of applications, such as text categorization (Joachims (1998)), image processing (Quinlan et al. (2004)), and bioinformatic problems (Brown et al. (1999)). Subsequent applications in time series prediction (M¨uller et al. (1999)) indicate the potential that SVMs have with respect to economics and finance. In predicting Australian foreign exchange rates, Kamruzzaman and Sarker (2003b) showed that a moving average-trained SVM has advantages over an Artificial Neural Network (ANN) based model, which was shown to have advantages over ARIMA models (2003a). Furthermore, Kamruzzaman et al. (2003) had a closer look at SVM regression and investigated how it performs with different standard kernel functions. It was found that Gaussian Radial Basis Function (RBF) and
polynomial kernels appear to be a better choice in forecasting the Australian foreign exchange market than linear or spline kernels. Although Gaussian kernels
are adequate measures of similarity when the representation dimension of the space remains small, they fail to reach their goal in high dimensional spaces (Francois et al. (2005)).We will examine the general ability of SVMs to correctly classify daily EUR/GBP, EUR/JPY and EUR/USD exchange rate directions. It is more useful for traders and risk managers to predict exchange rate fluctuations than their levels. To predict that the level of the EUR/USD, for instance, is close to the level today is trivial. On the contrary, to determine if the market will rise or fall is much more complex and interesting. Since SVM performance mostly depends on choosing the right kernel, we empirically verify the use of customized p-Gaussians by comparing them with a
range of standard kernels. The remainder is organized as follows: Section 2 outlines the procedure for obtaining an explanatory input dataset. Section 3 formulates the SVM as applied to exchange rate forecasting and presents the kernels used. Section 4 describes the benchmarks and trading metrics used for model evaluation. Section 5 gives the empirical results. The conclusion, as well as brief directions for future research, are given in Section 6.
Introduction
Support Vector Machines (SVMs) have proven to be a principled and very powerful supervised learning system that since its introduction (Cortes and Vapnik (1995)) has outperformed many systems in a variety of applications, such as text categorization (Joachims (1998)), image processing (Quinlan et al. (2004)), and bioinformatic problems (Brown et al. (1999)). Subsequent applications in time series prediction (M¨uller et al. (1999)) indicate the potential that SVMs have with respect to economics and finance. In predicting Australian foreign exchange rates, Kamruzzaman and Sarker (2003b) showed that a moving average-trained SVM has advantages over an Artificial Neural Network (ANN) based model, which was shown to have advantages over ARIMA models (2003a). Furthermore, Kamruzzaman et al. (2003) had a closer look at SVM regression and investigated how it performs with different standard kernel functions. It was found that Gaussian Radial Basis Function (RBF) and
polynomial kernels appear to be a better choice in forecasting the Australian foreign exchange market than linear or spline kernels. Although Gaussian kernels
are adequate measures of similarity when the representation dimension of the space remains small, they fail to reach their goal in high dimensional spaces (Francois et al. (2005)).We will examine the general ability of SVMs to correctly classify daily EUR/GBP, EUR/JPY and EUR/USD exchange rate directions. It is more useful for traders and risk managers to predict exchange rate fluctuations than their levels. To predict that the level of the EUR/USD, for instance, is close to the level today is trivial. On the contrary, to determine if the market will rise or fall is much more complex and interesting. Since SVM performance mostly depends on choosing the right kernel, we empirically verify the use of customized p-Gaussians by comparing them with a
range of standard kernels. The remainder is organized as follows: Section 2 outlines the procedure for obtaining an explanatory input dataset. Section 3 formulates the SVM as applied to exchange rate forecasting and presents the kernels used. Section 4 describes the benchmarks and trading metrics used for model evaluation. Section 5 gives the empirical results. The conclusion, as well as brief directions for future research, are given in Section 6.
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for two-observer bearings-only tracking
Ben-Lian Xu, Qing-Lan Chen, Zheng-Yi Wua, Zhi-Quan Wang
Abstract
This paper presents the analytic recursive formulas of Crame´r-Rao lower bound (CRLB) for the switching models system, in which the target moves either with a constant velocity or with a constant speed and a constant turn rate. For the case of two-observer bearings-only maneuvering target tracking, a reliable maneuver detection method is investigated and then utilized to approximate the theoretic CRLB. Finally, to demonstrate the agreement between the approximated CRLB using the proposed maneuver detection method and the theoretic one, a large number of Monte-Carlo runs under different maneuvering scenarios are conducted. Correctness of the analytic recursive formulas of CRLB and effectiveness of the proposed maneuver detection method are verified from these simulations.
1. Introduction
For a general nonlinear filtering problem, the optimal recursive state estimator in the Bayesian sense needs the complete posterior density of the state. As is well known, such a problem has no analytic closed-form solution. As a consequence, the solution of the nonlinear filtering is usually approximated by means of extended
Kalman filter, unscented Kalman filter [9], particle filter [16] and so on. In practical applications, many researchers usually attempt to derive the theoretically best achievable second-order error performance for nonlinear filtering to evaluate these filters, and we call it Crame´r-Rao lower bound (CRLB).
The Crame´r-Rao lower bound, defined as the inverse of the Fisher information matrix (FIM), represents an objective lower limit of cognizability of parameters in constant or random parameter estimation. It has been widely used in many cases, such as bearings-only tracking [13,20,21], ballistic target tracking [8] and so on. Due to its ability of predicting the best achievable performance, it is usually utilized as a benchmark to evaluate the performance of an estimation algorithm and can provide guidance to improve the experimental design as well. Therefore, the discussion on CRLB is always a hot topic in the field of target motion analysis (TMA).
A complete history of the developments of the CRLB for target tracking would involve more than sixty publications (e.g., [3–7,10,12–15,17–19]), and its attention varies from single target to multiple targets, from constant parameter estimation to random parameter estimation, from clutter-free to clutter, etc. In many practical tracking scenarios, due to high frequency of maneuver occurrence, the corresponding discussion
on the CRLB seems necessary and imminent. However, few reports on the CRLB of maneuvering target tracking can be found so far, even so, most of them are based on such an assumption that the model history is already known [13]. Therefore, the aim of this paper is to investigate the performance bounds of the switching-models-based bearings-only maneuvering target tracking system, in which the target moves either with a constant velocity or with a constant speed and a constant turn rate. It is noted that, in bearing-only tracking, if a single observer is collecting angular measurements, the target state becomes observable only if the observer ‘‘outmaneuvers” the target, i.e., observer motion is one derivative higher than that of target and a component of this motion is perpendicular to the line of sight. However, for a two-observer bearings-only tracking system to be discussed in this paper, it is not the case, and the target is always observable only if the target does not move on the line connecting the two observers, thus the observability will not be addressed later.
This work takes inspiration mainly from Farina et al. [7], in which the detection probability of target was supposed to be less than unity. Thus we suppose in this paper that there is the possibility of target’s maneuver or non-maneuver at each sampling period. On the basis of such assumption, the recursive theoretic formulas
of CRLB for two-observer bearings-only maneuvering target are derived. Since the calculation of the theoretic CRLB relies on the exponentially growing number of maneuver/non-maneuver sequences, a reliable maneuver detector is developed to reduce the possible sequences to further approximate the theoretic CRLB.
The rest of this paper is arranged as follows. The target dynamics and measurement models for bearingsonly target tracking are formulated in Section 2. In Section 3, the recursive theoretic formulas of CRLB for two-observer bearings-only maneuvering target are derived, and moreover, a reliable maneuver detector for bearings-only tracking is developed and utilized to approximate the theoretic CRLB. Finally, performance evaluation and conclusions are given in Sections 4 and 5, respectively.
1. Introduction
For a general nonlinear filtering problem, the optimal recursive state estimator in the Bayesian sense needs the complete posterior density of the state. As is well known, such a problem has no analytic closed-form solution. As a consequence, the solution of the nonlinear filtering is usually approximated by means of extended
Kalman filter, unscented Kalman filter [9], particle filter [16] and so on. In practical applications, many researchers usually attempt to derive the theoretically best achievable second-order error performance for nonlinear filtering to evaluate these filters, and we call it Crame´r-Rao lower bound (CRLB).
The Crame´r-Rao lower bound, defined as the inverse of the Fisher information matrix (FIM), represents an objective lower limit of cognizability of parameters in constant or random parameter estimation. It has been widely used in many cases, such as bearings-only tracking [13,20,21], ballistic target tracking [8] and so on. Due to its ability of predicting the best achievable performance, it is usually utilized as a benchmark to evaluate the performance of an estimation algorithm and can provide guidance to improve the experimental design as well. Therefore, the discussion on CRLB is always a hot topic in the field of target motion analysis (TMA).
A complete history of the developments of the CRLB for target tracking would involve more than sixty publications (e.g., [3–7,10,12–15,17–19]), and its attention varies from single target to multiple targets, from constant parameter estimation to random parameter estimation, from clutter-free to clutter, etc. In many practical tracking scenarios, due to high frequency of maneuver occurrence, the corresponding discussion
on the CRLB seems necessary and imminent. However, few reports on the CRLB of maneuvering target tracking can be found so far, even so, most of them are based on such an assumption that the model history is already known [13]. Therefore, the aim of this paper is to investigate the performance bounds of the switching-models-based bearings-only maneuvering target tracking system, in which the target moves either with a constant velocity or with a constant speed and a constant turn rate. It is noted that, in bearing-only tracking, if a single observer is collecting angular measurements, the target state becomes observable only if the observer ‘‘outmaneuvers” the target, i.e., observer motion is one derivative higher than that of target and a component of this motion is perpendicular to the line of sight. However, for a two-observer bearings-only tracking system to be discussed in this paper, it is not the case, and the target is always observable only if the target does not move on the line connecting the two observers, thus the observability will not be addressed later.
This work takes inspiration mainly from Farina et al. [7], in which the detection probability of target was supposed to be less than unity. Thus we suppose in this paper that there is the possibility of target’s maneuver or non-maneuver at each sampling period. On the basis of such assumption, the recursive theoretic formulas
of CRLB for two-observer bearings-only maneuvering target are derived. Since the calculation of the theoretic CRLB relies on the exponentially growing number of maneuver/non-maneuver sequences, a reliable maneuver detector is developed to reduce the possible sequences to further approximate the theoretic CRLB.
The rest of this paper is arranged as follows. The target dynamics and measurement models for bearingsonly target tracking are formulated in Section 2. In Section 3, the recursive theoretic formulas of CRLB for two-observer bearings-only maneuvering target are derived, and moreover, a reliable maneuver detector for bearings-only tracking is developed and utilized to approximate the theoretic CRLB. Finally, performance evaluation and conclusions are given in Sections 4 and 5, respectively.
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J. Duane Morningred, Duncan A. Mellichamp, and Dale E. Seborg
Department of Chemical and Nuclear Engineering
University of California
Santa Barbara, CA 93106
ABSTRACT
Although predictive control techniques such as Dynamic Matrix Control and Model Algorithmic Control have received much attention in recent years, systematic methods for on-line updating of the process models and predictive control laws raely have been rported. Such adaptation is desirable if the process conditions change significantly over a period of time. The same approach also could be used to generate a discrete convolution model for the initil controller design. In ftis paper, several altrative stategies for on-line aptation during closedloop opertion are evaluated and compared via simulations.
INTRODUCTION
Industrial process-control groups introduced the predictive control techniques of Dynamic Matrix Control (DMC) [1] and Model Algorithmic Control (MAC) [2,3], respectvely, nearly a decade ago. They were developed specifically for multipleinput, multiple-output (MIMO) control problems in which the process could be adequately described by linear dynamic models. These model-based techniques minimize predicted process deviations from the set point using quadratic performance indices. Thus, these methods were the first optimal controllers to be routinely used in the process industries. The evaluation and extension of these techniques spread quickly among academic circles as industry revived dying enthusiasm among researchers for studying and developing optimal controllers.
Since DMC and MAC were introduced, industrial and academic researchers have reported extensions and modifications of both control techniques. In addition, researchers have developed several new predictive controllers and have shown that they are robust under many circumstances. For example, Ogunnaike [4] gives a statistical interpretation of the robustness of DMC. Maurath et al [5] illustrate stability regions for a single-input, single-output (SISO) predictive controller. Garcia and Moari [6] show the stabilizing effect of filtering feedback errr for such controllers.
If the process characteristics change significantly, the predictive controller may need to be retuned or redesigned. This, naurly, leads one to consider adaptng the predictive controler on-line to accommodate the changing process conditions. In fact, one merely needs to estimate the process models on line to make many predictive controllers adaptive. The dearth of publicized industrial experiences extolling adaptive predictive controllers, though, suggests that implementation of these controllers may not be straightforward. Some of the pertinent questions that point to possible implementation problems are:
1. How does one decide if the current controller's performance is unsatisfactory?
2. How should a new process model and controller be obtined, if neded?
3. How should a good process model for the initial controller design be obtined?
This paper helps to answer these questions by presenting systematic methods for the on-line updating of the process models and control laws. The proposed strategies also can be used to develop the convolution model for the initial controler design. In the next section, existing predictive controller techniques are discussed briefly.
INTRODUCTION
Industrial process-control groups introduced the predictive control techniques of Dynamic Matrix Control (DMC) [1] and Model Algorithmic Control (MAC) [2,3], respectvely, nearly a decade ago. They were developed specifically for multipleinput, multiple-output (MIMO) control problems in which the process could be adequately described by linear dynamic models. These model-based techniques minimize predicted process deviations from the set point using quadratic performance indices. Thus, these methods were the first optimal controllers to be routinely used in the process industries. The evaluation and extension of these techniques spread quickly among academic circles as industry revived dying enthusiasm among researchers for studying and developing optimal controllers.
Since DMC and MAC were introduced, industrial and academic researchers have reported extensions and modifications of both control techniques. In addition, researchers have developed several new predictive controllers and have shown that they are robust under many circumstances. For example, Ogunnaike [4] gives a statistical interpretation of the robustness of DMC. Maurath et al [5] illustrate stability regions for a single-input, single-output (SISO) predictive controller. Garcia and Moari [6] show the stabilizing effect of filtering feedback errr for such controllers.
If the process characteristics change significantly, the predictive controller may need to be retuned or redesigned. This, naurly, leads one to consider adaptng the predictive controler on-line to accommodate the changing process conditions. In fact, one merely needs to estimate the process models on line to make many predictive controllers adaptive. The dearth of publicized industrial experiences extolling adaptive predictive controllers, though, suggests that implementation of these controllers may not be straightforward. Some of the pertinent questions that point to possible implementation problems are:
1. How does one decide if the current controller's performance is unsatisfactory?
2. How should a new process model and controller be obtined, if neded?
3. How should a good process model for the initial controller design be obtined?
This paper helps to answer these questions by presenting systematic methods for the on-line updating of the process models and control laws. The proposed strategies also can be used to develop the convolution model for the initial controler design. In the next section, existing predictive controller techniques are discussed briefly.
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Saturday, August 15, 2009
WORD 2002
FROM A TO Z
Active document
The active document is the document shown in the active window.
Active document window
The active document window is the document window that shows the Word document you’re currently working on. If you tell Word to print, for example, Word prints the document shown in the active document window. If you enter text by typing on the keyboard, Word enters what you type into the active document window.
Word adds a button to the taskbar for each open document. By clicking a document’s taskbar button, you can switch to that document.
Alignment
You can align the text, tables, and pictures that make up a Word document so that the item you’re aligning rests against the left edge of the page, is centered horizontally on the page, or rests against the right edge of the page.
Aligning Text
To align text, first select the text. Then use the toolbar’s Align Left, Center, Align Right, or Justify buttons to align the text.
If you’re using Personalized menus and toolbars, these alignment buttons may not all appear. You can still change alignment though. Select the text, choose the FormatP aragraphs command, and click the Indents And Spacing tab. Use this tab’s Alignment drop-down list box to select the desired alignment.
Aligning T Tables
To align a table, right-click the table and then choose the Table Properties command from the shortcut menu. When Word displays the Table Properties dialog box, click the Table tab and then use its Alignment buttons—Left, Center, and Right—to align the table.
Aligning Pictures
To align a picture, right-click the picture and then choose the Format Picture command from the shortcut menu. When Word displays the Picture Properties dialog box, click the Layout tab and then use its Horizontal Alignment buttons to align the picture.
Aligning Other Graphic Objects
You align other graphic objects—drawn objects, autoshapes, and so on—in the same way that you align a picture. Right-click the object and choose the Format command from the shortcut menu. When Word displays the Formatting dialog box, click its Layout tab. Then use the Layout tab’s alignment buttons to make your changes.
Animating Characters
You can animate, or add motion, to the characters used in a Word document. The animation won’t show up in printed copies of the document, of course. But online versions of the document displayed by Word and other Office programs will show the animation.
To animate characters, select the text and then choose the Format Font command. When Word displays the Font dialog box, click the Text Effects tab. Then use its Animations list box to select the animation effect you want.
The active document is the document shown in the active window.
Active document window
The active document window is the document window that shows the Word document you’re currently working on. If you tell Word to print, for example, Word prints the document shown in the active document window. If you enter text by typing on the keyboard, Word enters what you type into the active document window.
Word adds a button to the taskbar for each open document. By clicking a document’s taskbar button, you can switch to that document.
Alignment
You can align the text, tables, and pictures that make up a Word document so that the item you’re aligning rests against the left edge of the page, is centered horizontally on the page, or rests against the right edge of the page.
Aligning Text
To align text, first select the text. Then use the toolbar’s Align Left, Center, Align Right, or Justify buttons to align the text.
If you’re using Personalized menus and toolbars, these alignment buttons may not all appear. You can still change alignment though. Select the text, choose the FormatP aragraphs command, and click the Indents And Spacing tab. Use this tab’s Alignment drop-down list box to select the desired alignment.
Aligning T Tables
To align a table, right-click the table and then choose the Table Properties command from the shortcut menu. When Word displays the Table Properties dialog box, click the Table tab and then use its Alignment buttons—Left, Center, and Right—to align the table.
Aligning Pictures
To align a picture, right-click the picture and then choose the Format Picture command from the shortcut menu. When Word displays the Picture Properties dialog box, click the Layout tab and then use its Horizontal Alignment buttons to align the picture.
Aligning Other Graphic Objects
You align other graphic objects—drawn objects, autoshapes, and so on—in the same way that you align a picture. Right-click the object and choose the Format command from the shortcut menu. When Word displays the Formatting dialog box, click its Layout tab. Then use the Layout tab’s alignment buttons to make your changes.
Animating Characters
You can animate, or add motion, to the characters used in a Word document. The animation won’t show up in printed copies of the document, of course. But online versions of the document displayed by Word and other Office programs will show the animation.
To animate characters, select the text and then choose the Format Font command. When Word displays the Font dialog box, click the Text Effects tab. Then use its Animations list box to select the animation effect you want.
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INTRODUCTION
You should find PowerPoint 2002 From A to Z easy to use. You only need to know that the book organizes its information—key tasks and important terms—alphabetically in order to use the book. You’ll find it helpful, however, if you understand what this book assumes about your computer skills, what you should know about the PowerPoint program from the very start, and what editorial conventions this book uses. This short introduction provides this information.
What Y You ou Should Know About Windows
You don’t need to be computer expert to use either this book or Microsoft PowerPoint. Definitely not. But you want to be comfortable working with your computer and Microsoft Windows.
For example, you should know how to turn your computer on and off, how to start and stop programs, how to choose menu commands, and how to work with dialog boxes. This book, for the most part, doesn’t provide this Windows information.
If you need this Windows information, you need to take the Windows online tutorial, get a friend to give you a quick tutorial, or acquire another book on Windows.
TIPS
Any short book on Windows will tell you what you need to know, but if you’re a business user of Windows 2000 or Windows XP, you may want to look at the Effective Executive’s Guide to Windows 2000 or the Effective Executive’s Guide to Windows XP.
These books supply a tutorial on Windows geared for business professionals.
What Y You ou Should Know About PowerPoint
You don’t need to know anything about PowerPoint to use this book. But understanding from the very start how PowerPoint works and how the PowerPoint program window is laid out will help you immensely in your learning. Let me quickly provide this background information.
You should find PowerPoint 2002 From A to Z easy to use. You only need to know that the book organizes its information—key tasks and important terms—alphabetically in order to use the book. You’ll find it helpful, however, if you understand what this book assumes about your computer skills, what you should know about the PowerPoint program from the very start, and what editorial conventions this book uses. This short introduction provides this information.
What Y You ou Should Know About Windows
You don’t need to be computer expert to use either this book or Microsoft PowerPoint. Definitely not. But you want to be comfortable working with your computer and Microsoft Windows.
For example, you should know how to turn your computer on and off, how to start and stop programs, how to choose menu commands, and how to work with dialog boxes. This book, for the most part, doesn’t provide this Windows information.
If you need this Windows information, you need to take the Windows online tutorial, get a friend to give you a quick tutorial, or acquire another book on Windows.
TIPS
Any short book on Windows will tell you what you need to know, but if you’re a business user of Windows 2000 or Windows XP, you may want to look at the Effective Executive’s Guide to Windows 2000 or the Effective Executive’s Guide to Windows XP.
These books supply a tutorial on Windows geared for business professionals.
What Y You ou Should Know About PowerPoint
You don’t need to know anything about PowerPoint to use this book. But understanding from the very start how PowerPoint works and how the PowerPoint program window is laid out will help you immensely in your learning. Let me quickly provide this background information.
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Sunday, June 7, 2009
Over half of the students who enrol on economics degree courses have not studied mathematics beyond GCSE or an equivalent level. These include many mature students whose last encounter with algebra, or any other mathematics beyond basic arithmetic, is now a dim and distant memory. It is mainly for these students that this book is intended. It aims to develop their mathematical ability up to the level required for a general economics degree course (i.e. one not specializing in mathematical economics) or for a modular degree course in economics and related subjects, such as business studies. To achieve this aim it has several objectives.
First, it provides a revision of arithmetical and algebraic methods that students probably studied at school but have now largely forgotten. It is a misconception to assume that, just because a GCSE mathematics syllabus includes certain topics, students who passed examinations on that syllabus two or more years ago are all still familiar with the material. They usually require some revision exercises to jog their memories and to get into the habit of using the different mathematical techniques again. The first few chapters are mainly devoted to this revision, set out where possible in the context of applications in economics.
Second, this book introduces mathematical techniques that will be new to most students through examples of their application to economic concepts. It also tries to get students tackling problems in economics using these techniques as soon as possible so that they can see how useful they are. Students are not required to work through unnecessary proofs, or wrestle with complicated special cases that they are unlikely ever to encounter again. For example, when covering the topic of calculus, some other textbooks require students to plough through abstract theoretical applications of the technique of differentiation to every conceivable type of function and special case before any mention of its uses in economics is made. In this book, however, we introduce the basic concept of differentiation followed
by examples of economic applications in Chapter 8. Further developments of the topic,
such as the second-order conditions for optimization, partial differentiation, and the rules for differentiation of composite functions, are then gradually brought in over the next few chapters, again in the context of economics application.
Third, this book tries to cover those mathematical techniques that will be relevant to students’ economics degree programmes. Most applications are in the field of microeconomics, rather than macroeconomics, given the increased emphasis on business economics within many degree courses. In particular, Chapter 7 concentrates on a number of mathematical techniques that are relevant to finance and investment decision-making.
Given that most students now have access to computing facilities, ways of using a spreadsheet package to solve certain problems that are extremely difficult or time-consuming to solve manually are also explained.
Although it starts at a gentle pace through fairly elementary material, so that the students who gave up mathematics some years ago because they thought that they could not cope with A-level maths are able to build up their confidence, this is not a watered-down ‘mathematics without tears or effort’ type of textbook. As the book progresses the pace is increased and students are expected to put in a serious amount of time and effort to master the material. However, given the way in which this material is developed, it is hoped that students will be motivated to do so. Not everyone finds mathematics easy, but at least it helps if you can see the reason for having to study it.
First, it provides a revision of arithmetical and algebraic methods that students probably studied at school but have now largely forgotten. It is a misconception to assume that, just because a GCSE mathematics syllabus includes certain topics, students who passed examinations on that syllabus two or more years ago are all still familiar with the material. They usually require some revision exercises to jog their memories and to get into the habit of using the different mathematical techniques again. The first few chapters are mainly devoted to this revision, set out where possible in the context of applications in economics.
Second, this book introduces mathematical techniques that will be new to most students through examples of their application to economic concepts. It also tries to get students tackling problems in economics using these techniques as soon as possible so that they can see how useful they are. Students are not required to work through unnecessary proofs, or wrestle with complicated special cases that they are unlikely ever to encounter again. For example, when covering the topic of calculus, some other textbooks require students to plough through abstract theoretical applications of the technique of differentiation to every conceivable type of function and special case before any mention of its uses in economics is made. In this book, however, we introduce the basic concept of differentiation followed
by examples of economic applications in Chapter 8. Further developments of the topic,
such as the second-order conditions for optimization, partial differentiation, and the rules for differentiation of composite functions, are then gradually brought in over the next few chapters, again in the context of economics application.
Third, this book tries to cover those mathematical techniques that will be relevant to students’ economics degree programmes. Most applications are in the field of microeconomics, rather than macroeconomics, given the increased emphasis on business economics within many degree courses. In particular, Chapter 7 concentrates on a number of mathematical techniques that are relevant to finance and investment decision-making.
Given that most students now have access to computing facilities, ways of using a spreadsheet package to solve certain problems that are extremely difficult or time-consuming to solve manually are also explained.
Although it starts at a gentle pace through fairly elementary material, so that the students who gave up mathematics some years ago because they thought that they could not cope with A-level maths are able to build up their confidence, this is not a watered-down ‘mathematics without tears or effort’ type of textbook. As the book progresses the pace is increased and students are expected to put in a serious amount of time and effort to master the material. However, given the way in which this material is developed, it is hoped that students will be motivated to do so. Not everyone finds mathematics easy, but at least it helps if you can see the reason for having to study it.
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Sunday, May 31, 2009
Adobe Photoshop, or simply Photoshop, is a graphics editing program (also known as a DPP, Desktop Publishing Program) developed and published by Adobe Systems. It is the current and primary market leader for commercial bitmap and image manipulation, and is the flagship product of Adobe Systems. It has been described as "an industry standard for graphics professionals" and was one of the early "killer applications" on the PC.
Adobe's 2005 "Creative Suite" rebranding led to Adobe Photoshop 8's renaming to Adobe Photoshop CS. Thus, Adobe Photoshop CS4 is the 11th major release of Adobe Photoshop. The CS rebranding also resulted in Adobe offering numerous software packages containing multiple Adobe programs for a reduced price. Adobe Photoshop is included in most of Adobe's Creative Suite offerings.
Photoshop's popularity, combined with its high retail price, makes Photoshop's piracy rate relatively high. Adobe countered by including SafeCast DRM starting with Adobe Photoshop CS.
Development
Early history
In 1987, Thomas Knoll, a PhD student at the University of Michigan, began writing a program on his Macintosh Plus to display grayscale images on a monochrome display. This program, called Display, caught the attention of his brother John Knoll, an Industrial Light & Magic employee, who recommended Thomas turn it into a full-fledged image editing program. Thomas took a six month break from his studies in 1988 to collaborate with his brother on the program, which had been renamed ImagePro. Later that year, Thomas renamed his program Photoshop and worked out a short-term deal with scanner manufacturer Barneyscan to distribute copies of the program with a slide scanner; a "total of about 200 copies of Photoshop were shipped" this way.
During this time, John traveled to Silicon Valley and gave a demonstration of the program to engineers at Apple Computer Inc. and Russell Brown, art director at Adobe. Both showings were successful, and Adobe decided to purchase the license to distribute in September 1988. While John worked on plug-ins in California, Thomas remained in Ann Arbor writing program code. Photoshop 1.0 was released in 1990 for Macintosh exclusively.
Features
Photoshop has strong ties with other Adobe software for media editing, animation, and authoring. Files in Photoshop's native format, .PSD, can be exported to and from Adobe Illustrator, Adobe Premiere Pro, After Effects, and Adobe Encore to make professional standard DVDs and provide non-linear editing and special effects services, such as backgrounds, textures, and so on, for television, film, and the Web. For example, Photoshop CS broadly supports making menus and buttons for DVDs. For .PSD files exported as a menu or button, it only needs to have layers, nested in layer sets with a cuing format, and Adobe Encore DVD reads them as buttons or menus. Photoshop is a pixel-based image editor, unlike Adobe Illustrator, which is a vector-based image editor.
Photoshop can utilize the color models RGB, lab, CMYK, grayscale, binary bitmap, and duotone. Photoshop has the ability to read and write raster and vector image formats such as .EPS, .PNG, .GIF, .JPEG, and Fireworks. It also has several native file formats:
• The .PSD (Photoshop Document) format stores an image with support for most imaging
options available in Photoshop. These include layers with masks, color spaces, ICC
profiles, transparency, text, alpha channels and spot colors, clipping paths, and
duotone settings. This is in contrast to many other file
formats (e.g. .EPS or .GIF) that restrict content to provide streamlined,
predictable functionality. Photoshop's popularity means that the .PSD format is
widely used, and it is supported to some extent by most competing software.
• The .PSB (Large Document Format) format is a newer version of .PSD designed for
files over 2 gigabytes.
• The .PDD (PhotoDeluxe Document) format is a version of .PSD that only supports the
features found in the discontinued PhotoDeluxe software.
CS4
Photoshop CS4 features additions such as the ability to paint directly on 3D models, wrap 2D images around 3D shapes, convert gradient maps to 3D objects, add depth to layers and text, get print-quality output with the new ray-tracing rendering engine, and enjoy exporting to supported common 3D formats; the new Adjustment and Mask Panels; Content-aware scaling (also known as seam carving); Fluid Canvas Rotation and File display options. On 30 April, Adobe released Photoshop CS4 Extended, which includes all the same features of Adobe Photoshop CS4 with the addition of capabilities for scientific imaging, 3D, and high end film and video users. The successor to Photoshop CS3, Photoshop CS4, is the first 64-bit Photoshop on consumer computers.
Plugins
Main article: Photoshop plugin
Photoshop functionality can be extended by add-on programs called Photoshop plugins which act like mini-editors that modify the image. The most common type are filter plugins that provide various image effects. They are located in the 'Filter' menu.
Trademark
Adobe discourages use of "Photoshop" as a verb, as in using photoshopping to refer to photo editing, to prevent its trademark from becoming a genericized trademark. Nevertheless, photoshop is commonly used as a verb. Also commonly shortened to "shopped," "chopped," or "shooped," these terms have become the modern replacement of the term "airbrushed" when used to describe photo retouching in general. (Prior to digital photography, airbrushes were used to apply pigments to photographic negatives to enhance or obscure detail.)
Consumer market
While Photoshop is the industry standard image editing program for professional raster graphics and other digital art, its relatively high suggested retail price has led to a number of competing graphics tools, such as GIMP and Paint.NET, being made available at lower prices or as freeware for the amateur market. To compete in this market, and to counter unusually high rates of piracy of its high end products, the company introduced a consumer-oriented version of Photoshop as Adobe Photoshop Elements. A more user-friendly interface and new tools such as the "red-eye" reduction brush were aimed firmly at the more casual image editor. Many professional features were omitted. Removing CMYK functionality, for example, made Elements unsuitable for commercial prepress work.
Adobe's 2005 "Creative Suite" rebranding led to Adobe Photoshop 8's renaming to Adobe Photoshop CS. Thus, Adobe Photoshop CS4 is the 11th major release of Adobe Photoshop. The CS rebranding also resulted in Adobe offering numerous software packages containing multiple Adobe programs for a reduced price. Adobe Photoshop is included in most of Adobe's Creative Suite offerings.
Photoshop's popularity, combined with its high retail price, makes Photoshop's piracy rate relatively high. Adobe countered by including SafeCast DRM starting with Adobe Photoshop CS.
Development
Early history
In 1987, Thomas Knoll, a PhD student at the University of Michigan, began writing a program on his Macintosh Plus to display grayscale images on a monochrome display. This program, called Display, caught the attention of his brother John Knoll, an Industrial Light & Magic employee, who recommended Thomas turn it into a full-fledged image editing program. Thomas took a six month break from his studies in 1988 to collaborate with his brother on the program, which had been renamed ImagePro. Later that year, Thomas renamed his program Photoshop and worked out a short-term deal with scanner manufacturer Barneyscan to distribute copies of the program with a slide scanner; a "total of about 200 copies of Photoshop were shipped" this way.
During this time, John traveled to Silicon Valley and gave a demonstration of the program to engineers at Apple Computer Inc. and Russell Brown, art director at Adobe. Both showings were successful, and Adobe decided to purchase the license to distribute in September 1988. While John worked on plug-ins in California, Thomas remained in Ann Arbor writing program code. Photoshop 1.0 was released in 1990 for Macintosh exclusively.
Features
Photoshop has strong ties with other Adobe software for media editing, animation, and authoring. Files in Photoshop's native format, .PSD, can be exported to and from Adobe Illustrator, Adobe Premiere Pro, After Effects, and Adobe Encore to make professional standard DVDs and provide non-linear editing and special effects services, such as backgrounds, textures, and so on, for television, film, and the Web. For example, Photoshop CS broadly supports making menus and buttons for DVDs. For .PSD files exported as a menu or button, it only needs to have layers, nested in layer sets with a cuing format, and Adobe Encore DVD reads them as buttons or menus. Photoshop is a pixel-based image editor, unlike Adobe Illustrator, which is a vector-based image editor.
Photoshop can utilize the color models RGB, lab, CMYK, grayscale, binary bitmap, and duotone. Photoshop has the ability to read and write raster and vector image formats such as .EPS, .PNG, .GIF, .JPEG, and Fireworks. It also has several native file formats:
• The .PSD (Photoshop Document) format stores an image with support for most imaging
options available in Photoshop. These include layers with masks, color spaces, ICC
profiles, transparency, text, alpha channels and spot colors, clipping paths, and
duotone settings. This is in contrast to many other file
formats (e.g. .EPS or .GIF) that restrict content to provide streamlined,
predictable functionality. Photoshop's popularity means that the .PSD format is
widely used, and it is supported to some extent by most competing software.
• The .PSB (Large Document Format) format is a newer version of .PSD designed for
files over 2 gigabytes.
• The .PDD (PhotoDeluxe Document) format is a version of .PSD that only supports the
features found in the discontinued PhotoDeluxe software.
CS4
Photoshop CS4 features additions such as the ability to paint directly on 3D models, wrap 2D images around 3D shapes, convert gradient maps to 3D objects, add depth to layers and text, get print-quality output with the new ray-tracing rendering engine, and enjoy exporting to supported common 3D formats; the new Adjustment and Mask Panels; Content-aware scaling (also known as seam carving); Fluid Canvas Rotation and File display options. On 30 April, Adobe released Photoshop CS4 Extended, which includes all the same features of Adobe Photoshop CS4 with the addition of capabilities for scientific imaging, 3D, and high end film and video users. The successor to Photoshop CS3, Photoshop CS4, is the first 64-bit Photoshop on consumer computers.
Plugins
Main article: Photoshop plugin
Photoshop functionality can be extended by add-on programs called Photoshop plugins which act like mini-editors that modify the image. The most common type are filter plugins that provide various image effects. They are located in the 'Filter' menu.
Trademark
Adobe discourages use of "Photoshop" as a verb, as in using photoshopping to refer to photo editing, to prevent its trademark from becoming a genericized trademark. Nevertheless, photoshop is commonly used as a verb. Also commonly shortened to "shopped," "chopped," or "shooped," these terms have become the modern replacement of the term "airbrushed" when used to describe photo retouching in general. (Prior to digital photography, airbrushes were used to apply pigments to photographic negatives to enhance or obscure detail.)
Consumer market
While Photoshop is the industry standard image editing program for professional raster graphics and other digital art, its relatively high suggested retail price has led to a number of competing graphics tools, such as GIMP and Paint.NET, being made available at lower prices or as freeware for the amateur market. To compete in this market, and to counter unusually high rates of piracy of its high end products, the company introduced a consumer-oriented version of Photoshop as Adobe Photoshop Elements. A more user-friendly interface and new tools such as the "red-eye" reduction brush were aimed firmly at the more casual image editor. Many professional features were omitted. Removing CMYK functionality, for example, made Elements unsuitable for commercial prepress work.
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Tuesday, May 26, 2009
A denial-of-service attack (DoS attack) or distributed denial-of-service attack (DDoS attack) is an attempt to make a computer resource unavailable to its intended users. Although the means to carry out, motives for, and targets of a DoS attack may vary, it generally consists of the concerted efforts of a person or persons to prevent an Internet site or service from functioning efficiently or at all, temporarily or indefinitely. Perpetrators of DoS attacks typically target sites or services hosted on high-profile web servers such as banks, credit card payment gateways, and even root nameservers.
One common method of attack involves saturating the target (victim) machine with external communications requests, such that it cannot respond to legitimate traffic, or responds so slowly as to be rendered effectively unavailable. In general terms, DoS attacks are implemented by either forcing the targeted computer(s) to reset, or consuming its resources so that it can no longer provide its intended service or obstructing the communication media between the intended users and the victim so that they can no longer communicate adequately.
Denial-of-service attacks are considered violations of the IAB's Internet Proper Use Policy. They also commonly constitute violations of the laws of individual nations.
Means
As there are two main types of attack (wired and wireless), different material is to be used for each of the two types.
Attacks on wired networks require a great deal of computing power, often even requiring the need of distributed computing. Attacks on wired networks do not require any NICs or external antennae, yet often do have the need of a (broadband) connection to the Internet.
Attacks on wireless networks require a high power NIC and usually a high-gain (directional) external antenna (to increase range as well as power output). High power NICs fall in the range of the 300mW-cards. Examples can be found from companies such as Demarc Technology Group.
Manifestations
The United States Computer Emergency Readiness Team defines symptoms of denial-of-service attacks to include:
* Unusually slow network performance (opening files or accessing web sites)
* Unavailability of a particular web site
* Inability to access any web site
* Dramatic increase in the number of spam emails received—(this type of DoS attack is considered an e-mail bomb)
Denial-of-service attacks can also lead to problems in the network 'branches' around the actual computer being attacked. For example, the bandwith of a router between the Internet and a LAN may be consumed by an attack, compromising not only the intended computer, but also the entire network.
If the attack is conducted on a sufficiently large scale, entire geographical regions of Internet connectivity can be compromised without the attacker's knowledge or intent by incorrectly configured or flimsy network infrastructure equipment.
Methods of attack
A "denial-of-service" attack is characterized by an explicit attempt by attackers to prevent legitimate users of a service from using that service. Attacks can be directed at any network device, including attacks on routing devices and web, electronic mail, or Domain Name System servers.
A DoS attack can be perpetrated in a number of ways. The five basic types of attack are:
1. Consumption of computational resources, such as bandwidth, disk space, or processor time
2. Disruption of configuration information, such as routing information.
3. Disruption of state information, such as unsolicited resetting of TCP sessions.
4. Disruption of physical network components.
5. Obstructing the communication media between the intended users and the victim so that they can no longer communicate adequately.
A DoS attack may include execution of malware intended to:
* Max out the processor's usage, preventing any work from occurring.
* Trigger errors in the microcode of the machine.
* Trigger errors in the sequencing of instructions, so as to force the computer into an unstable state or lock-up.
* Exploits errors in the operating system to cause resource starvation and/or thrashing, i.e. to use up all available facilities so no real work can be accomplished.
* Crash the operating system itself.
* iFrame (D)DoS, in which an HTML document is made to visit a webpage with many KB's of information many times, until they achieve the amount of visits to where bandwidth limit is exceeded.
ICMP flood
See also: Smurf attack, Ping flood, and Ping of death
A smurf attack is one particular variant of a flooding DoS attack on the public Internet. It relies on misconfigured network devices that allow packets to be sent to all computer hosts on a particular network via the broadcast address of the network, rather than a specific machine. The network then serves as a smurf amplifier. In such an attack, the perpetrators will send large numbers of IP packets with the source address faked to appear to be the address of the victim. The network's bandwidth is quickly used up, preventing legitimate packets from getting through to their destination. To combat Denial of Service attacks on the Internet, services like the Smurf Amplifier Registry have given network service providers the ability to identify misconfigured networks and to take appropriate action such as filtering.
Ping flood is based on sending the victim an overwhelming number of ping packets, usually using the "ping -t" command from unix like hosts (the -t flag on Windows systems has a far less malignant function). It is very simple to launch, the primary requirement being access to greater bandwidth than the victim.
SYN flood sends a flood of TCP/SYN packets, often with a forged sender address. Each of these packets is handled like a connection request, causing the server to spawn a half-open connection, by sending back a TCP/SYN-ACK packet, and waiting for a packet in response from the sender address. However, because the sender address is forged, the response never comes. These half-open connections saturate the number of available connections the server is able to make, keeping it from responding to legitimate requests until after the attack ends.
Teardrop attack
A Teardrop attack involves sending mangled IP fragments with overlapping, over-sized, payloads to the target machine. A bug in the TCP/IP fragmentation re-assembly code of various operating systems causes the fragments to be improperly handled, crashing them as a result of this.[4] Windows 3.1x, Windows 95, and Windows NT operating systems, as well as versions of Linux prior to versions 2.0.32 and 2.1.63 are vulnerable to this attack.
Peer-to-peer attacks
Attackers have found a way to exploit a number of bugs in peer-to-peer servers to initiate DDoS attacks. The most aggressive of these peer-to-peer-DDoS attacks exploits DC++. Peer-to-peer attacks are different from regular botnet-based attacks. With peer-to-peer there is no botnet and the attacker does not have to communicate with the clients it subverts. Instead, the attacker acts as a 'puppet master,' instructing clients of large peer-to-peer file sharing hubs to disconnect from their peer-to-peer network and to connect to the victim’s website instead. As a result, several thousand computers may aggressively try to connect to a target website. While a typical web server can handle a few hundred connections/sec before performance begins to degrade, most web servers fail almost instantly under five or six thousand connections/sec. With a moderately big peer-to-peer attack a site could potentially be hit with up to 750,000 connections in a short order. The targeted web server will be plugged up by the incoming connections. While peer-to-peer attacks are easy to identify with signatures, the large number of IP addresses that need to be blocked (often over 250,000 during the course of a big attack) means that this type of attack can overwhelm mitigation defenses. Even if a mitigation device can keep blocking IP addresses, there are other problems to consider. For instance, there is a brief moment where the connection is opened on the server side before the signature itself comes through. Only once the connection is opened to the server can the identifying signature be sent and detected, and the connection torn down. Even tearing down connections takes server resources and can harm the server.
This method of attack can be prevented by specifying in the p2p protocol which ports are allowed or not. If port 80 is not allowed, the possibilities for attack on websites can be very limited.
Permanent denial-of-service attacks
A permanent denial-of-service (PDoS), also known loosely as phlashing, is an attack that damages a system so badly that it requires replacement or reinstallation of hardware. Unlike the distributed denial-of-service attack, a PDoS attack exploits security flaws in the remote management interfaces of the victim's hardware, be it routers, printers, or other networking hardware. These flaws leave the door open for an attacker to remotely 'update' the device firmware to a modified, corrupt or defective firmware image, therefore "bricking" the device and making it permanently unusable for its original purpose. The PDoS is a pure hardware targeted attack which can be much faster and requires fewer resources than using a botnet in a DDoS attack. Because of these features, and the potential and high probability of security exploits on Network Enabled Embedded Devices (NEEDs), this technique has come to the attention of numerous hacker communities such as Hack a Day.
PhlashDance is a tool created by Rich Smith (an employee of Hewlett-Packard's Systems Security Lab) used to detect and demonstrate PDoS vulnerabilities at the 2008 EUSecWest Applied Security Conference in London.
Application level floods
On IRC, IRC floods are a common electronic warfare weapon.
Various DoS-causing exploits such as buffer overflow can cause server-running software to get confused and fill the disk space or consume all available memory or CPU time.
Other kinds of DoS rely primarily on brute force, flooding the target with an overwhelming flux of packets, oversaturating its connection bandwidth or depleting the target's system resources. Bandwidth-saturating floods rely on the attacker having higher bandwidth available than the victim; a common way of achieving this today is via Distributed Denial of Service, employing a botnet. Other floods may use specific packet types or connection requests to saturate finite resources by, for example, occupying the maximum number of open connections or filling the victim's disk space with logs.
A "banana attack" is another particular type of DoS. It involves redirecting outgoing messages from the client back onto the client, preventing outside access, as well as flooding the client with the sent packets.
An attacker with access to a victim's computer may slow it until it is unusable or crash it by using a fork bomb.
Nuke
A Nuke is an old denial-of-service attack against computer networks consisting of fragmented or otherwise invalid ICMP packets sent to the target, achieved by using a modified ping utility to repeatedly send this corrupt data, thus slowing down the affected computer until it comes to a complete stop.
In online gaming, nuking is used by spamming another user, or all other users, with random repeated messages in quick succession. Such techniques are also seen in instant messaging programs as repeatedly sending text can be assigned to a macro or AppleScript. Modern operating systems are usually resistant to these nuke attacks, and online games now have third party "Flood control."
A specific example of a nuke attack that gained some prominence is the WinNuke, which exploited the vulnerability in the NetBIOS handler in Windows 95. A string of out-of-band data was sent to TCP port 139 of the victim's machine, causing it to lock up and display a Blue Screen of Death.
Distributed attack
A distributed denial of service attack (DDoS) occurs when multiple systems flood the bandwidth or resources of a targeted system, usually one or more web servers. These systems are compromised by attackers using a variety of methods.
Malware can carry DDoS attack mechanisms; one of the more well known examples of this was MyDoom. Its DoS mechanism was triggered on a specific date and time. This type of DDoS involved hardcoding the target IP address prior to release of the malware and no further interaction was necessary to launch the attack.
A system may also be compromised with a trojan, allowing the attacker to download a zombie agent (or the trojan may contain one). Attackers can also break into systems using automated tools that exploit flaws in programs that listen for connections from remote hosts. This scenario primarily concerns systems acting as servers on the web.
Stacheldraht is a classic example of a DDoS tool. It utilizes a layered structure where the attacker uses a client program to connect to handlers, which are compromised systems that issue commands to the zombie agents, which in turn facilitate the DDoS attack. Agents are compromised via the handlers by the attacker, using automated routines to exploit vulnerabilities in programs that accept remote connections running on the targeted remote hosts. Each handler can control up to a thousand agents.
These collections of systems compromisers are known as botnets. DDoS tools like stacheldraht still use classic DoS attack methods centered on IP spoofing and amplification like smurf attacks and fraggle attacks (these are also known as bandwidth consumption attacks). SYN floods (also known as resource starvation attacks) may also be used. Newer tools can use DNS servers for DoS purposes. (see next section)
Unlike MyDoom's DDoS mechanism, botnets can be turned against any IP address. Script kiddies use them to deny the availability of well known websites to legitimate users. More sophisticated attackers use DDoS tools for the purposes of extortion — even against their business rivals.
It is important to note the difference between a DDoS and DoS attack. If an attacker mounts a smurf attack from a single host it would be classified as a DoS attack. In fact, any attack against availability would be classed as a Denial of Service attack. On the other hand, if an attacker uses a thousand zombie systems to simultaneously launch smurf attacks against a remote host, this would be classified as a DDoS attack.
The major advantages to an attacker of using a distributed denial-of-service attack are that multiple machines can generate more attack traffic than one machine, multiple attack machines are harder to turn off than one attack machine, and that the behavior of each attack machine can be stealthier, making it harder to track down and shut down. These attacker advantages cause challenges for defense mechanisms. For example, merely purchasing more incoming bandwidth than the current volume of the attack might not help, because the attacker might be able to simply add more attack machines.
Although most DDoS attacks are malicious in nature, the same technique can be used to aid the Internet community. Internet fraud schemes, such as Nigerian 419 scams or phishing, commonly involve fraudulent websites that either impersonate a real website for purposes of stealing the victim's identity, or lend credibility to a scammer's fictional business venture to lure the victim into a false sense of confidence. Scam baiters, who combat these scams by posing as victims for the purpose of wasting the scammer's time and money and obtaining information that can be used by authorities, will forward sites they encounter during the course of their conversations to groups that specialize in site-killing.[citation needed] The group will first try to have a site taken down by informing the host of said site that the site is being used fraudulently. In the case where that approach fails, the group will organize a "takedown" of the site by encouraging its members to visit the site en masse and continually refresh its content (an intentional form of the Slashdot effect sometimes referred to as flash mobbing, although that term is technically reserved for real-world gatherings). Alternately, some groups have special web pages that link to images hosted by these fake sites and show the images to visitors (usually members or supporters of the site-killing group) while constantly reloading them, which is known as intentional bandwidth hogging.[citation needed] The purpose, similar to malicious DoS attacks, is to (a.) rapidly consume all of the website's allocated monthly bandwidth, after which requests for the site's content are refused, (b.) draw the attention of the site's host, who when faced with the constant onslaught on the entire hosting network's resources, will usually remove the site, and/or (c.) take up all available connections and maximum throughput of the host so that would-be victims cannot access the site.
Reflected attack
A distributed reflected denial of service attack (DRDoS) involves sending forged requests of some type to a very large number of computers that will reply to the requests. Using Internet protocol spoofing, the source address is set to that of the targeted victim, which means all the replies will go to (and flood) the target.
ICMP Echo Request attacks (Smurf Attack) can be considered one form of reflected attack, as the flooding host(s) send Echo Requests to the broadcast addresses of mis-configured networks, thereby enticing many hosts to send Echo Reply packets to the victim. Some early DDoS programs implemented a distributed form of this attack.
Many services can be exploited to act as reflectors, some harder to block than others. DNS amplification attacks involve a new mechanism that increased the amplification effect, using a much larger list of DNS servers than seen earlier.
Degradation-of-service attacks
"Pulsing" zombies are compromised computers that are directed to launch intermittent and short-lived floodings of victim websites with the intent of merely slowing it rather than crashing it. This type of attack, referred to as "degradation-of-service" rather than "denial-of-service", can be more difficult to detect than regular zombie invasions and can disrupt and hamper connection to websites for prolonged periods of time, potentially causing more damage than concentrated floods.[16][17] Exposure of degradation-of-service attacks is complicated further by the matter of discerning whether the attacks really are attacks or just healthy and likely desired increases in website traffic.
Unintentional denial of service
This describes a situation where a website ends up denied, not due to a deliberate attack by a single individual or group of individuals, but simply due to a sudden enormous spike in popularity. This can happen when an extremely popular website posts a prominent link to a second, less well-prepared site, for example, as part of a news story. The result is that a significant proportion of the primary site's regular users — potentially hundreds of thousands of people — click that link in the space of a few hours, having the same effect on the target website as a DDoS attack.
News sites and link sites — sites whose primary function is to provide links to interesting content elsewhere on the Internet — are most likely to cause this phenomenon. The canonical example is the Slashdot effect. Sites such as Digg, the Drudge Report, Fark, Something Awful, and the webcomic Penny Arcade have their own corresponding "effects", known as "the Digg effect", being "drudged", "farking", "goonrushing" and "wanging"; respectively.
Routers have also been known to create unintentional DoS attacks, as both D-Link and Netgear routers have created NTP vandalism by flooding NTP servers without respecting the restrictions of client types or geographical limitations.
Similar unintentional denials of service can also occur via other media, e.g. when a URL is mentioned on television. If a server is being indexed by Google or another search engine during peak periods of activity, or does not have a lot of available bandwidth while being indexed, it can also experience the effects of a DoS attack.
Legal action has been taken in at least one such case. In 2006, Universal Tube & Rollform Equipment Corporation has sued YouTube: massive numbers of would-be youtube.com users accidentally typed the tube company's URL, utube.com; as a result, the tube company ended up having to spend large amounts of money on upgrading their bandwidth.
Denial-of-Service Level II
The goal of DoS L2 (possibly DDoS) attack is to cause a launching of a defense mechanism which blocks the network segment from which the attack originated. In case of distributed attack or IP header modification (that depends on the kind of security behavior) it will fully block the attacked network from Internet, but without system crash.
Blind Denial of Service
Blind Denial of Service attacks are particularly pernicious. With a blind attack the attacker has a significant advantage. If the attacker must be able to receive traffic from the victim, then he must either subvert the routing fabric or use his own IP address. Either provides an opportunity for the victim to track the attacker and/or filter out his traffic. With a blind attack the attacker can use forged IP addresses, making it extremely difficult for the victim to filter out their packets. The TCP SYN flood attack is an example of a blind attack. Designers should make every attempt possible to prevent blind denial of service attacks.
Incidents
The first major attack involving DNS servers as reflectors occurred in January 2001. The target was Register.com.[21] This attack, which forged requests for the MX records of AOL.com (to amplify the attack) lasted about a week before it could be traced back to all attacking hosts and shut off. It used a list of tens of thousands of DNS records that were a year old at the time of the attack.
In February, 2001, the Irish Government's Department of Finance server was hit by a denial of service attack carried out as part of a student campaign from NUI Maynooth. The Department officially complained to the University authorities and a number of students were disciplined.[citation needed]
In July 2002, the Honeynet Project Reverse Challenge was issued. The binary that was analyzed turned out to be yet another DDoS agent, which implemented several DNS related attacks, including an optimized form of a reflection attack.
On two occasions to date, attackers have performed DNS Backbone DDoS Attacks on the DNS root servers. Since these machines are intended to provide service to all Internet users, these two denial of service attacks might be classified as attempts to take down the entire Internet, though it is unclear what the attackers' true motivations were. The first occurred in October 2002 and disrupted service at 9 of the 13 root servers. The second occurred in February 2007 and caused disruptions at two of the root servers.[citation needed]
In February 2007, more than 10,000 online game servers in games such as Return to Castle Wolfenstein, Halo, Counter-Strike and many others were attacked by "RUS" hacker group. The DDoS attack was made from more than a thousand computer units located in the republics of the former Soviet Union, mostly from Russia, Uzbekistan and Belarus. Minor attacks are still continuing to be made today.[citation needed]
In the weeks leading up to the five-day 2008 South Ossetia war, a DDoS attack directed at Georgian government sites containing the message: “win+love+in+Rusia" effectively overloaded and shut down multiple Georgian servers. Websites targeted included the Web site of the Georgian president, Mikhail Saakashvili, rendered inoperable for 24 hours, and the National Bank of Georgia. While heavy suspicion was placed on Russia for orchestrating the attack through a proxy, the St. Petersburg-based criminal gang known as the Russian Business Network, or R.B.N, the Russian government denied the allegations, stating that it was possible that individuals in Russia or elsewhere had taken it upon themselves to start the attacks.
Performing DoS-attacks
A wide array of programs are used to launch DoS-attacks. Most of these programs are completely focused on performing DoS-attacks, while others are also true Packet Injector, thus able to perform other tasks as well.
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