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How often you set logcheck to run will depend on how much data you receive in the email. If you have an active system, we recommend that you increase the frequency of the logcheck runs. If you have a relatively small system, running logcheck once a day will produce a manageable email that can be handled when things are quiet.

The clipped center of gravity method: For this approach, each membership function is clipped at the corresponding rule firing strengths. The centroid of the composite area is calculated and the horizontal coordinate is used as the output of the controller. This approach to centroid calculation is illustrated in Figure 19.2(e). The calculation of the centroid of the trapezoidal areas depends on whether the domain of the functions is discrete or continuous. For a discrete domain of a finite number of values, n, the output of the defuzzification process is calculated as (E) has its algebraic meaning) output = In the case of a continuous domain (f| has its algebraic meaning), output = where X is the universe of discourse.

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The square brackets imply that the second and third fields are optional. The default class is IN for Internet. Other values for class refer to little used name services which will not be considered here. Each resource record is an association of a name with an item of data. The type of association is identified by the assoc field, which is one of the set A, PTR, NS, etc. For example hostl 86400 IN A 10.20.30.40 hostl 86400 IN CNAME host2 hostl 86400 IN MX 10 mailhost or simply hostlA 10.20.30.40 hostl CNAME host2 hostl MX 10 mailhost Since we need two sets of data, both for name lookups and address lookups, there are records in which name and data are host names and host numbers, respectively, and another set in which those roles are reversed, e.g. 40 PTR hostl In addition to mapping host names to addresses and vice versa, the DNS tables also tell e-mail services how to deliver mail. We will need to have a so-called 'mail-exchanger' record in the

Another popular log analyzer is webalizer. Webalizer was specifically written to produce an HTML page with graphing statistics for access to a web site. Figure 7-1 shows a webalizer page for a busy site.

Figure 7-1: Webalizer output The webalizer page is quite long and contains information on the amount of traffic served; how many hits per month, per day, per hour; ranking of the most popular pages; and so on. To access specific information about a certain month, you can click its entry and you will be presented with a screen similar to Figure 7-2.

8:

Figure 7-2: Month output in webalizer Webalizer outputs all of its data by default into /var/lib/webalizer, which is linked to /srv/www/htdocs/webalizer for serving via Apache.

This chapter presented an overview of fuzzy rule based reasoning systems. In the next chapter a specific example of such systems is discussed, namely fuzzy controllers.

DNS tables in order to tell e-mail which host handles e-mail for the domain. An entry of the form domain-name MX priority mailhost

This allows you to automate the running of webalizer on a foreign system and, at the same time, allows you to access the results via the web server you are analyzing. We will not talk about the configuration of webalizer here, as the configuration file /etc/ webalizer.conf is extremely well documented, and the default suits 99 percent of people who need to analyze their web traffic.

This chapter has covered what logging on a SUSE Linux system means and what you can do with the messages that are generated both by the kernel and processes that you run. However, reading log files is a skill in itself. There is no good way to teach people how to read log files; rather, it is something that comes with experience. However, we will give you are short rundown of common entries you will find in /var/log/messages and how to at least interpret them to help you on your way. The following line is an example of a log entry indicating an SSH login failure:

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