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Classes communicate with each other in much the same way that real physical objects in the world interact Strongly typed: This means that a Java program cannot arbitrarily access the host computer's memory Memory access by Java programs is limited to specific, controlled areas having particular representations Type safety is verified when code is loaded into the JVM by the Byte Code Verifier (see 2, "The Base Java Security Model: The Original Applet Sandbox") In addition, runtime checks on type safety (such as checks for array bound overflow, type incompatibility, and local-versus-remote code security policy) are all handled by the Java Virtual Machine As we shall see, type safety is essential for Java security In fact, a majority of serious Java security attacks target the type system Multi-threaded: Java programs can execute more than one task at the same time.

The remainder of the chapter is organized as follows. Section 2.2, Wavelets and Scaling: Theory, discusses in detail the key properties of the wavelet coef cients of scaling processes. It starts with a brief, yet precise, introduction to the continuous and discrete wavelet transforms, to the multiresolution analysis theory underlying the latter, and the low complexity decomposition algorithm made possible by it. It recalls concisely the de nitions of two of the main paradigms of scaling self-similarity and long-range dependence. The properties of the wavelet coef cients of self-similar, long-range-dependent, and fractal processes are then given, and it is shown how the analysis of these various kinds of scaling can be gathered into a single framework within the wavelet representation. Extensions to more general classes of scaling processes requiring a collection of scaling exponents, such as multifractals, are also discussed. The aim of Section 2.3, Wavelets and Scaling: Estimation, is to indicate how and why this wavelet framework enables the ef cient analysis of scaling processes. This is achieved through the introduction of the logscale diagram, where the key analysis tasks of the detection of scaling interpretation of the nature of scaling and estimation of scaling parameters can be performed. Practical issues in the use of

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the logscale diagram are addressed, with references to examples from real traf c data and arti cially generated traces. De nitions, statistical performance, and pertinent features of the estimators for scaling parameters are then studied in detail. The logscale diagram, rst de ned with respect to second-order statistical quantities, is then extended to statistics of other orders. It is also indicated how the tool allows for and deals with situations=processes departing from pure scaling, such as superimposed deterministic nonstationarities. Finally, clear connections between the wavelet tool and a number of more classical statistical tools dedicated to the analysis of scaling are drawn, showing how the latter can be pro tably generalized in their wavelet incarnations. Section 2.4, Wavelet and Scaling: Synthesis, proposes a wavelet-based synthesis of the fractional Brownian motion. It shows how this process can be naturally and ef ciently expanded in a wavelet basis, allowing, provided that the wavelets are suitably designed, its accurate and computationally ef cient implementation. Finally, in Section 2.5, Wavelets and Scaling: Perspectives, a brief indication is given of what may lay ahead in the broad land of scaling and wavelets. 2.2 2.2.1 WAVELET AND SCALING: THEORY Wavelet Analysis: A Brief Introduction

Sarbanes-Oxley and other external regulations requiring adequate controls render container-based security obsolete for auditable records as well as for data, personal medical information, and personally identifying information. Authorization must be designed and technically implemented in a way that can control access rights to a single field in a database or a single file in a data storage area. If your authorization and access controls cannot do that and at the same time provide an audit trail as to who may have made the last change in the field or file, they are hardly adequate in the current regulatory environment, let alone the rules that may be promulgated in the near term. Take stock of your current access control situation, particularly the quality of your authorization process and how well your existing access controls are capable of enforcing the rights granted. Very few existing implementations establish a distinct, tightly controlled relationship between individual end users and finite data elements. For example, it would not be appropriate for any supervisor in the company to see every employee s Social Security number in a human resources application. Privacy advocates would argue that even HR staff should not be able to see it once entered into the income tax reporting data field.

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