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This unfortunately common strategy is known as penetrate and patch, and has been criticized for many years by security practitioners (For more on this issue, see [McGraw, 1998]) Better software engineering and more thorough software assurance practices are much more appealing It is even possible to write your own code defensively so it makes a much harder target for bad guys to attack (see both the guidelines of 7, "Java Security Guidelines: Developing and Using Java More Securely," and 9, "The Future of Java Security: Challenges Facing Mobile Code") The addition of code signing to Java in JDK 11 and its extension with access control in Java 2 allow for the creation of complex security policies based on authentication of applet signers Using this technology, a Web surfer could specify a list of trusted sites whose applets should be allowed to run without restrictions.

contiguous nonoverlapping blocks of length T1 T2 (except possibly for the last block), and for each block j P 1; N compute the aggregate traf c V1 , V2 over the subintervals of length T1 , T2 . For l; l H P 1; 8 , let hl P 0; N denote the total number of blocks such that L1 V1 l and let hlH P 0; hl denote the size of the subset of those blocks such that L2 V2 l H . Then PrfL2 l H jL1 lg hl H : hl

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these two states It is theoretically the same as the building construction example if you said you have an existing state, a beautifully wooded 10 acres in the Keweenaw Peninsula of Michigan, and a fixed budget Your desired state is to have a 2,000-square-foot log home on the property, along with a garage and a pole barn that blend into the forest The buildings should be made mostly from on-site materials and must be built within the budget The documentation that allows moving from the existing state to the desired state is brought about by the construction architecture creative design, a process where design concepts are expressed in detailed drawings and specification documents.

Figure 18.2 shows the estimated conditional probability densities for a 1:05, 1.95 traf c for time scales 500 ms, 1 s, and 5 s. In the following, T1 T2 .

The trick is creating a sound security policy and correctly coding it into your browser The next few sections discuss various kinds of malicious applets Starting with the least worrisome category-the merely annoying-the text progresses through the truly malicious machine-hangers Possible motives for creating these applets are discussed along the way Keep in mind while you read this chapter that the malicious applets described here pale in comparison with the attack applets described in 5 Fortunately, the security researchers who discovered those vulnerabilities are the good guys..

Fig. 18.2 Top row: Probability densities with L2 conditioned on L1 for a 1:05. Bottom row: Probability densities with L2 conditioned on L1 for a 1:95.

For the aggregate throughput traces with a 1:05 Figure 18.2 (top row) the three-dimensional (3D) conditional probability densities can be seen to be skewed diagonally from the lower left side toward the upper right side. This indicates that if the current traf c level L1 is low, say, L1 1, chances are that L2 will be low as well. That is, the probability mass of PrfL2 jL1 1g is concentrated toward 1. Conversely, the plots show that PrfL2 jL1 8g is concentrated toward 8. This is more clearly seen in Fig. 18.3(a), which shows two cross sections, that is, 2D projections, re ecting PrfL2 jL1 1g and PrfL2 jL1 8g. For the aggregate throughput traces with a 1:95 (Fig. 18.2 (bottom-row)), on the other hand, the shape of the distribution does not change as the conditioning variable L1 is varied. This is more clearly seen in the projections of PrfL2 jL1 1g and PrfL2 jL1 8g shown in Fig. 18.3(b). This indicates that for a 1:95 traf c observing the past (over the time scales considered) does not help much in predicting the future beyond the information conveyed by the xed a priori distribution. Given the de nition of Lk , the Gaussian shape of the marginal densities is consistent with short-range correlations, making the central limit theorem approximately applicable over larger time scales. In both cases a 1:05, 1.95), the shape of the distribution stays relatively constant across a wide range of time scales 500 ms to 20 s. For a 1:35, 1.65 the predictability structure lies ``in-between'' (not shown here).

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