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Attempts to crash the application or its network stack, such as the Ping of Death, which attempts to overflow the network stack of PCs with excessively long packets that require reassembly on the receiving end. Connection attempts that fill up a service s queue and render the service inaccessible to other clients. The most well known are SYN attacks (named for the synchronize sequence numbers, or SYN step, in a TCP connection setup) that take down TCP-based services by flooding the input queue with bogus connection requests. The trin00 attacks in February 2000 that affected ebay.com and yahoo.com were distributed denial-of-service attacks that generated enormous connection loads for the Internet-facing web servers of those sites. Attempts to start multiple copies of a service, consuming all available resources on the server. Usually an attacker will look for a well-known, frequently used CGI script or executable, and then connect to that URL in an attempt to have the server fork itself to death.



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results of these simulations show that the DAR model does a good job of estimating the cell-loss rate when 16 sources are multiplexed. Similar results were obtained for the other sequences [18]. Now we consider connection admission control (CAC). Since the DAR model is a Markov chain model of the source, it conforms to one of the sets of conditions a source model must have for the effective bandwidth (EBW) theory of Elwalid and Mitra [8]. Moreover, the DAR model is a reversible Markov chain, and so it inspired a powerful extension of the EBW method, called the Chernoff-dominated eigenvalue (CCE) method [7]. Suppose we have a switch that can process at rate C (Mb=s) and has a buffer of size B (ms). We want to nd the maximum number of statistically homogeneous sources that can be admitted while keeping the cell-loss rate no larger than 10 6 . The CDE method gives an approximate analytic solution with known error bounds; this solution is denoted by KCDE. Another way to obtain the solution is to test candidate values by evaluating the cell-loss rate by simulation; we treat this as the exact solution and denote it by Ksim. Table 12.3 compares the results of the CDE method to the CAC found from simulations. The number admitted by the CDE method is a very close approximation to the ``true'' value obtained by simulation. This implies that the DAR model captures enough of the statistical properties of the trace to produce good admission decisions.





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In addition to the obvious issue of careful packet filtering and closing known security holes in your web and application servers, your best defense against a DoS attack is to make sure your servers can handle very high connection backlogs, and that they discard half-completed connections quickly:

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The DAR model may not be suitable for a single source (by a single source we mean a source that does not interact with other sources) as described above. Lucantoni et al. [22] give three areas where single source models are useful: studying what types of traf c descriptors make sense for parameter negotiation with the network at call setup, testing rate control algorithms, and predicting the quality of service degradation caused by congestion on an access link. For this reason, Heyman [11] proposed the GBAR model. Lakshman et al. [21] use the GBAR model to predict frame sizes in a rate control algorithm. Lucantoni et al. [22] propose a Markov-renewal process model to describe a single source. This model has the advantage of being very general, and the disadvantage that it is not parameterized by some simple summary statistics of the data trace. The GBAR model exploits the properties enjoyed by teleconferencing traf c described in Section 12.2.2, the geometrically decaying autocorrelation

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