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The CSC-SSM card, commonly called the Anti-X card, can look for a variety of attacks as well as set up many policies that apply to FTP, web, and e-mail traffic However, by default no traffic is forwarded to the card: you must set up a policy to have traffic processed by the card For traffic that should be processed by the CSC-SSM card, the traffic is forwarded from the backplane of the ASA into the CSC card, processed by the card, and then forwarded back to the backplane of the ASA for further processing (assuming that the traffic isn t dropped by the CSC card because of a policy violation) You can see the traffic flow in Figure 10-4 Traffic that the card can process includes FTP, HTTP, HTTPS, SMTP, and POP3 It is not recommended to forward other types of traffic, because the card will probably drop these The following two sections will discuss how to set up a policy to have the CSC-SSM card process traffic CSC-SSM Configuration After you ve identified what traffic you want the card to process with a layer 3/4 class map, you can associate the CSC-SSM policy to it with the following configuration:

.

ciscoasa(config)# policy-map policy_map_name ciscoasa(config-pmap)# class class_map_name ciscoasa(config-pmap-c)# csc {fail-open | fail-close}

early Performing the selection early that is, directly on s would require doing a scan of all tuples in s It is probably cheaper, in this case, to compute the join by using the index, and then to reject tuples that fail the selection The projection operation, like the selection operation, reduces the size of relations Thus, whenever we need to generate a temporary relation, it is advantageous to apply immediately any projections that are possible This advantage suggests a companion to the perform selections early heuristic: Perform projections early It is usually better to perform selections earlier than projections, since selections have the potential to reduce the sizes of relations greatly, and selections enable the use of indices to access tuples An example similar to the one used for the selection heuristic should convince you that this heuristic does not always reduce the cost Drawing on the equivalences discussed in Section 1431, a heuristic optimization algorithm will reorder the components of an initial query tree to achieve improved query execution We now present an overview of the steps in a typical heuristic optimization algorithm You can understand the heuristics by visualizing a query expression as a tree, as illustrated in Figure 143 1 Deconstruct conjunctive selections into a sequence of single selection operations This step, based on equivalence rule 1, facilitates moving selection operations down the query tree 2 Move selection operations down the query tree for the earliest possible execution This step uses the commutativity and distributivity properties of the selection operation noted in equivalence rules 2, 7a, 7b, and 11 For instance, this step transforms (r 1 s) into either (r) 1 s or r 1 (s) whenever possible Performing value-based selections as early as possible reduces the cost of sorting and merging intermediate results The degree of reordering permitted for a particular selection is determined by the attributes involved in that selection condition 3 Determine which selection operations and join operations will produce the smallest relations that is, will produce the relations with the least number of tuples Using associativity of the 1 operation, rearrange the tree so that the leaf-node relations with these restrictive selections are executed rst This step considers the selectivity of a selection or join condition Recall that the most restrictive selection that is, the condition with the smallest selectivity retrieves the fewest records This step relies on the associativity of binary operations given in equivalence rule 6 4 Replace with join operations those Cartesian product operations that are followed by a selection condition (rule 4a) The Cartesian product operation is often expensive to implement since r1 r2 includes a record for each combination of records from r1 and r2 The selection may signi cantly reduce the number of records, making the join much less expensive than the Cartesian product.

Use a bonus formula rate (see Figures 5-40 to 5-42) to eliminate the cap and gaps in payout steps, which are both prominent features of the step bonus formula

The McGraw Hill Companies, 2001

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