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Some applications are made available to any user on the Internet through selfenrollment via Web interfaces. An end user enters all of his identity information from a Web browser anywhere on the World Wide Web. This information can be moved from its original storage point on the Web server, entered into the appropriate identity vault, and passed to a service directory for future authentication. It is OK to do this, but it s important to realize that you are taking a risk that the person using self-enrollment could be someone other than who he is purporting to be. Many of the free e-mail services available to anyone over the Internet are examples of self-enrollment processes. Without some other business process to verify identity or to perform reliable background vetting to prove the user information was keyed in or authorized by the person whose information is entered, self-enrollment should be limited to accessing only open or public information. The self-enrollment process can

CHAPTER SECTIONS: 1 / 2 / 3 / 4 / 5 / 6

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Figure 12.4 shows that the mean queue lengths in an in nite buffer computed from ve GBAR paths are similar to the mean queue lengths computed using the data. In Fig. 12.4 the vertical axis on the left shows the mean queue length in cells. Video quality is poor when the cell delays are large; 100 ms is an upper bound on the acceptable delay at a node in a network that provides video services. Two buffer drain times are also shown; the practical region for the maximum is below and to the left of the 100 ms line. The range of the mean queue lengths shown exceeds the practical region for maximum queue length. In the practical region, the model and the trace give very similar mean delays. The differences between the mean queue length from the GBAR model and the mean queue lengths from the data would be even smaller if a nite buffer were imposed. (This is the truncating effects of nite buffers that is described in Section 12.4.2). The comparisons for sequences B, C, and D are qualitatively the same as for sequence A.

Now we turn to more dynamic sequences, such as lms, news, sports, and entertainment television. Since the main purpose of the models is to aid in network performance evaluations, we are particularly interested in using the models to predict cell-loss rates.

Most problems discussed in 4, "Malicious Applets: Avoiding a Common Nuisance," and 5, "Attack Applets: Exploiting Holes in the Security Model," involve specific vulnerabilities that have more to do with the current implementation of Java than with the Java security model itself. There are, however, some general concerns raised by the computer security community regarding Java. Many of these concerns are not new; in fact, we wrote about a majority of these issues in 1996. We discuss what progress has been made since then (if any). This section discusses some of these concerns and how addressing them would improve Java security.

Broadcast VBR-coded video has different bit-rate characteristics than VBRcoded video conferences. Video conference sequences consist of head-and-shoulders pictures with little or no panning, while broadcast video is characterized by a succession of scenes. With interframe coding, it is clear that scene changes require more bits than intrascene frames, so broadcast video will differ from video conferences in at least this respect; this was demonstrated by Yasuda et al. [32]. There are some other differences too, as demonstrated by Verbiest et al. [31] and further ampli ed by Verbiest and Pinnoo [30]. In these papers, a DPCM-based coding algorithm is used. In the latter paper, it is shown that the number of bits per frame has a different autocorrelation function for broadcast video than for video conferences or video telephony. The autocorrelation functions for the last two are similar to each other and decay geometrically to zero. For broadcast video, the autocorrelation function does not decay to zero. Moreover, the rst frame after a scene change has signi cantly more bits than other frames in the scene. Ramamurthy and Sengupta [26] observe that the correlation function declines more rapidly at small lags than at large lags, and that the time series can be described by a semiMarkov process that has states identi ed by the bit rates for different types of scenes (and a state for scene changes). We build on this idea [12]; the simple DAR and GBAR models that described video conferences are not suf cient for broadcast video, although the DAR model is used as a building block in a more complex model. 12.3.1 Modeling Broadcast Video

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