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It is likely that protection is already in place for many of the vulnerabilities on your list. You should still include those, as they will help make your case for implementing protection against other vulnerabilities. Once you have the list, and it should be as long as possible, rate each vulnerability on two characteristics: likelihood and impact. Use a simple scale for each characteristic, such as a rating from 1 to 3; there s no reason to go out and research the expected dollar loss to the enterprise for each type of outage at this early stage. We offer a very basic example in Table 4.1.

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For the term in square brackets, we have the representation (6.49). It can be veri ed that gb arD r = 1 r 3 0 for r 4 1. The fact that D r is the unique zero of the contraction equation (6.55) with the property that D r 5 0 for r 4 1 nally implies that lim E e rN t;r =a er t=a :

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Table 4.1 Worksheet Describing Vulnerabilities, Likelihoods, and Impacts at a Fictitious Organization COMMENTS Critical systems already have mirrored disks. Systems are not clustered; a failed CPU will result in major downtime for one server. Corruption in a critical database could shut down the web site for hours. Only an issue if we lose data from our disks. Then it could be quite serious. We have a very complex network, with many single points of failure. Could cause the loss of our entire computing infrastructure. Won t damage data, but could keep us down for a long time. This area has a history of flooding. With the data center in a low floor, results could be catastrophic. An interstate highway goes within 200 yards of the front door. Always a small risk. Would have to hit the building to be a major problem. Can, of course, be a major disaster. Unlikely, but if it happened, could be serious.

6:72

LIKELIHOOD IMPACT LEVEL OF CONCERN (1-3) (1-3) (LIKELIHOOD IMPACT)

The convergence in distribution of N t; r follows after application of the convergence theorem for Laplace Stieltjes transforms [20]. The statements concerning H t; r follow immediately from those for N t; r . j REMARK 6.6.2. The distribution of the stochastic variable N t ; t ! 0, and similarly of H t ; t ! 0, is a n-stable distribution (cf. Samorodnitsky and Taqqu [33, p. 5]). The process fN t ; t ! 0g is a process with stationary independent increments. As is evident from its LST representation, it is self-similar with index 1=n, that is, N bt and b1=n N t ; b > 0, have the same distribution for every t > 0. Note that fH t ; t ! 0g is not self-similar. In Samorodnitsky and Taqqu [33] this process fN t ; t ! 0g with 1 < n < 2 is called a n-stable Levy motion with independent self-similar increments; for n 2, it is the Brownian motion. In the M =G=1 case, it is veri ed in Cohen [20] (using the fact that the process fht ; t ! 0g has stationary independent increments) that the nite-dimensional distributions of the fN t; r ; t ! 0g process converge to those of the fN t ; t ! 0g process.

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