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Exact detrending is expected to occur in the case of polynomial trends, but it is worthwhile to remark that the procedure still remains effective in the case of nonpolynomial trends, including many power-law trends, and oscillatory functions In Fig 26 an example is given of an FGN series contaminated with linear and sinusoidal trends Although the logscale diagram with N 2 is free from the effects of the linear trend (top right), higher values of N are required to effectively remove the sinusoidal trend (bottom right) Note that increasing N unfortunately also decreases the number of scales available for the analysis The usefulness of an ability to effectively remove smooth nonpolynomial trends is again illustrated in Fig 2.

From god@heaven.com Wed Jul 24 19:33:56 1996 Return-Path: <god@heaven.com> Received: from tigger.mediasoft.net by rstcorp.com (4.1/SMI-4.1) id AA21199; Wed, 24 Jul 96 19:33:54 EDT Received: from rstcorp.com (gem@tigger.mediasoft.net[205.139.200.246]) by tigger.mediasoft.net (8.6.12/8.6.9) with SMTP id SAA00966 for gem@rstcorp.com; Wed, 24 Jul 1996 18:30:31 -0400 Date: Wed, 24 Jul 1996 18:30:31 -0400 From: god@heaven.com Message-Id: <199607242230.SAA00966@tigger.mediasoft.net> Apparently-To: gem@rstcorp.com Status: RO This is forged mail.

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7, where, using a value of N as low as 2, a change in mean level in Ethernet byte data is shown to not affect the estimation of the exponent a of long-range dependence See Veitch and Abry [74], Fig 9, for further discussion The data set is derived from the ``pOct'' Bellcore trace [43] The versatility of wavelets with respect to the freedom of choice of N makes them an easy and ef cient tool for trend removal This contrasts with other methods, such as the Whittle estimator and parametric estimators in general, whose performance is heavily affected by trends (see Abry and Veitch [5, Fig 2 and Table 1]) An important advantage is hat one can choose N to eliminate preselected trends, without having to know if they are actually present a priori nor to jointly estimate their characteristics 234.

N OT E Each of the documents discussed in this chapter plays a specific role within the architecture. Each also has its limitations. For example, when it comes to access controls, there is little room in today s regulated companies for guidelines. Access controls must be derived from strong policies, detailed standards, and tightly defined procedures. If your company is trying to use guidelines for handling authentication and access controls, there is great risk that audits will result in findings that fail your control strategy.

2 Time Varying Scaling Exponents Because of the high variability inherent in scaling processes, instances of scaling behavior can incorrectly be judged as ``nonstationarity,'' in the broad sense of unstructured time variation, or conversely, variability due to nonstationarity may be erroneously taken to be scaling in nature..

Fig. 2.6 Robustness with respect to trends. Left: Synthesized FGN with H 0:80 (top), and with superimposed sinusoidal and linear trends (bottom). Right: Logscale diagrams of the data with trends using a Daubechies2 (i.e., N 2) wavelet (top) and Daubechies9 wavelet (bottom). It is seen that increasing N allows the effects of superimposed trends to be removed from the logscale diagram, enabling uncontaminated estimates of the scaling exponent. Here, ^ ^ with N 9, a 0:59 and therefore H 0:795.

Applets provide an interesting new twist on the standard approach to mail forging. Because applets load across the network and run on a Web surfer's machine, a mail forging applet can cause the standard SMTP daemon monitoring port 25 to report mail coming from the Web surfer's machine-not the machine serving the applet. This can be leveraged to doubly forge mail. Imagine that Alice hits a Web page and an applet is consequently run on her machine. By using Alice's machine to forge mail from Alice-that is, to forge mail apparently both from her machine and from her account on that machine-the doubly forged mail appears not to have been forged at all! With many standard server configurations, this forging attack is possible. Figure 4.4 illustrates the difference between the standard port 25 attack and the revised applet attack.

There is a strong need therefore to monitor the value of scaling parameters over time to distinguish between these possibilities, and to test if constancy of scaling can be concluded or not. A basic approach is to split a data set into a number m of adjacent blocks, and to examine the corresponding logscale diagrams. If alignment is found ^ in each over roughly the same range of scales, then separate estimations a m of the scaling exponent could be performed for each block. A null hypothesis might be that the (unknown) values a m share a common value. The dif culty is how to combine these estimates in a well-de ned statistical test, given that the problematic statistical nature of scaling processes would in general imply strong correlations between the ^ a m . Because of the quasi-decorrelation in the wavelet plane, however, it can be ^ shown [77] that the a m can be treated as almost independent, and moreover that they are approximately normally distributed with known variances. The dif cult problem of the constancy of the scaling exponent can thereby be reduced to a simple

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