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The modi cation (F7) gives excellent results (for continuous non-analytic functions one usually has an accuracy two or three gures less than machine precision) To illustrate this, we apply the modi ed approach to the Laplace transform (F6) for he M/D/1 queue with service time D = 1 and traf c intensity = 08 In Table F2 the values of f (t) = P {Wq > t} are given for t = 1, 5, 10, 25 and 50 The results in Table F2 are accurate in all displayed decimals (13 to 15 decimals) The calculations were done with M = 64, = 1, m = 4M, b = 22/m and n = 8 The inverse discrete FFT method was used to compute the g from (F4) In sharp contrast with the accuracy of the modi ed approach (F7), I found for the M/D/1 example the values 055607 and 055527 for P {Wq > t} with t = 1 when using the unmodi ed Den Iseger inversion algorithm and the Abate Whitt algorithm These values give accuracy to only three decimal places In the Abate Whitt algorithm I took a = 191, m = 11 and n = 38 (I had to increase n to 5500 to get the value 05548948 accurate to ve decimal places) The M/D/1 example shows convincingly how useful is the modi cation (F7) A scaling procedure In applied probability problems one is often interested in calculating very small probabilities, eg probabilities in the range of 10 12 or smaller In many cases asymptotic expansions are very useful for this purpose, but it may also be possible to use Laplace inversion with a scaling procedure Such a scaling procedure was proposed in Choudhury and Whitt (1997) The idea of the procedure is very simple Suppose that the function f (t) is non-negative and that the (very small) function value f (t0 ) is required at the point t0 > 0 The idea is to transform f (t) into the scaled function fa0 ,a1 (t) = a0 e a1 t f (t), t 0.

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Protocol anomaly detection provides some very powerful capabilities that make it an excellent mechanism for performing network intrusion detection First and foremost, because it does not require any prior signature to detect certain classes of attacks, it provides the ability to detect some zero-day attacks even before signatures are published This eliminates the window of vulnerability often created during the first hours or days after an exploit is published Another useful feature of the protocol anomaly detection technique is its esistance to evasion through polymorphism and other similar evasion techniques Since they do not rely on matching an explicit pattern, variations in the attack generally do not cause a failure to detect as they can in signature systems As they do not require constant signature updates, protocol anomaly detection systems can also provide a solution with lower administrative overhead Other solutions require weekly or even daily updates, often causing significant reductions in utility if these updates are not performed Even in modest networks, this level of maintenance quickly becomes a significant barrier to use Depending on the implementation, a protocol anomaly detection system may also scale better at higher network speeds The amount of comparison that needs to be performed is much smaller and much more static, therefore it is possible to implement very efficient models for detection One notable limitation of anomaly systems in general, however, is that they often provide less specific information than comparable explicit matching systems They usually identify items of concern descriptively or by some type of classification For example, a protocol anomaly detection system monitoring HTTP traffic may report observing a questionably encoded URL, while a signature system (properly updated) may report the same event as "Code Purple" While these labels are often fairly arbitrary names assigned to threats or exploits, once widely published they do provide a useful function by allowing security administrators to easily communicate about the threat or search for patches This is a fairly important consideration given the amount of coordination required across organisations during incident response situations This limitation is not a significant one, however Through various forms of classification work, a protocol anomaly detection system can be structured such that once anomalies are identified, additional work is performed to more specifically identify the event and provide additional reference information This can include common names, CVE (Common Vulnerabilities and Exposures) reference, BugTraq IDs, CERT advisories, and so on Done correctly, this can actually be quite a scalable approach, as the detection methods can be fairly lightweight and the data reduction prior to classification can be significant.

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