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Java 2 clearly introduces significant changes to the Java security landscape It is likely that the days of black-and-white security policy for mobile code are numbered With the major changes to Java's security architecture come a number of important responsibilities, the most important of which is mobile code policy creation and management The tools are still primitive, but the policy itself is essential Also essential to any mobile code system that makes use of code signing is solid key management capability Although the subject of public key infrastructure (PKI) is really beyond the scope of this book, we at least invoke some important concepts.

the predictability mechanism for on-line, per-connection estimation. In Section 18.5 we show performance results of SAC and show its ef cacy under different longrange dependence conditions and when the number of SAC connections is varied. We conclude with a discussion of current results and future work.

Why You Need an Assessment Policy What to Include in an Assessment Policy Sample Assessment Policy Statements

18.2 18.2.1

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Managers responsible for setting and maintaining policies based on signed code will encounter issues including choice of certificate authority, who to issue keys to, how to ensure that private keys are kept private, whether to get a corporate key and how to protect it, how to disable keys of employees who leave an organization, where to store keys, and so on These are nontrivial issues that have yet to be worked out in the real world Hopefully, widespread support for code-signing systems will quickly appear on consumer desktops worldwide Truthfully, the PKI is much less mature than many security researchers and pundits predicted it would be by now This is partly because deploying an effective PKI is much more difficult than it sounds.

Let Xt tPZ be a time series, which, for example, represents the trace of data ow at a bottleneck link measured at some xed time granularity. We de ne the aggregated series Xi m as Xi m 1 X Xim : m im m 1

That is, Xt is partitioned into blocks of size m, their values are averaged, and i is used to index these blocks. Let r k and r m k denote the autocorrelation functions of Xt and Xi m , respectively. Xt is self-similar more precisely, asymptotically second-order selfsimilar if the following conditions hold: r k $ const k b ; r m k $ r k ; 18:1 18:2

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But it is also at least partially due to the greed of certificate authorities who chose to charge developers for identities (public/private key pairs) instead of issuing them for free and charging elsewhere for their use Without a solid PKI, systems like Java 2 Java may take a while to catch on We predict that signed mobile code will find its most pervasive use among early adopters as an intranet technology (as opposed to an Internet technology) Of course, we're very well prepared to be wrong about that For a long time, Java developers have wanted some way in which less restriction could be placed on their applets At the same time, managers in many enterprises have been searching for ways to manage code (not just mobile code, but any code) more securely In its Java 2 guise, Java offers a powerful answer to these needs..

for k and m large, where 0 < b < 1. That is, Xt is ``self-similar'' in the sense that the correlation structure is preserved with P respect to time aggregation relation (18.2) and r k behaves hyperbolically with I r k I as implied by Eq. (18.1). The k 0 latter property is referred to as long-range dependence. Let H 1 b=2. H is called the Hurst parameter, and by the range of b, 1 < H < 1. It follows from Eq. (18.1) that the farther H is away from 1 the more 2 2 long-range dependent Xt is, and vice versa. Thus, the Hurst parameter acts as an indicator of the degree of self-similarity. A test for long-range dependence can be obtained by checking whether H signi cantly deviates from 1 or not. We use two methods for testing this condition. 2 The rst method, the variance time plot, is based on the slowly decaying variance of a self-similar time series. The second method, the R=S plot, use the fact that for a self-similar time series, the rescaled range or R=S statistic grows according to a power law with exponent H as a function of the number of points included. Thus, the plot of R=S against this number on a log log scale has a slope that is an estimate of H. A comprehensive discussion of the estimation methods can be found in Beran [4] and Taqqu et al. [39].

A random variable X has a heavy-tailed distribution if PrfX > xg $ x a as x 3 I, where 0 < a < 2. That is, the asymptotic shape of the tail of the distribution obeys a power law. The Pareto distribution, p x ak a x a 1 ; with parameters a > 0; k > 0, x ! k, has the distribution function PrfX xg 1 k=x a ;

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