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"This is one of those rare books that you will read to learn about the product and keep rereading to find those tidbits that you missed before" Gary Bushey, SharePoint Server MVP Content Management Server (CMS) is fast becoming a vital content-management tool that helps administrators and developers handle the ever-increasing amount of content on their Web sites However, an authoritative source of product information has been missing until now Microsoft Content Management Server 2002: A Complete Guide is the first book that explains how to effectively unlock the power of CMS Administrators and developers alike will learn how to enhance their Web servers' scalability, flexibility, and extensibility by using CMS features and API to manage content on the Microsoft platform This thorough reference explains the product architecture, then shows you how to create and edit content and use the workflow Topic coverage includes CMS administration and security, the Publishing API, deployment options, site configuration, and template development A running example is used throughout the book to illustrate how important CMS features are Using the New real-world Web dialog box, we can name the find answers gallery questions implemented inTemplate Gallerysite development Inside you'llnew template to such and provide a description for it The name should be easily understood by the content authors The as: description is optional and is limited to 255 characters in length Parent Gallery is a read-only field that shows the full path to the parent gallery from the root of the template galleries will talk about customize tab later; hierarchy We6: How do you the Rights workflow click OK to create the new gallery 10: How do you develop template gallery called BOTS Consulting (Figure 2002 For our sample site, we have created afor Microsoft Content Management erver (CMS) 9-12) using Visual Studio NET 17: How do you establish user rights .

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An anomaly is defined as something different, abnormal, peculiar or not easily classified While this concept can be applied to virtually anything, we are interested in how it applies to computer security In this context, an anomaly can be defined as some action or data that is not considered normal for a given system, user or network This definition still covers a fairly wide range and can include such things as traffic patterns, user activity and application behavior The belief is that at least a significant portion of threats or concerning conditions should manifest themselves as anomalies and thus be detectable The general approach used by anomaly detection is that something (ie, a network, a host, a set of users, etc) is observed and compared against expected behavior If variation from the expected is noted, that variation is flagged as an anomaly Often this includes some measure of the scope or nature of the variation and serves to drive further investigation, alerting, and analysis One of the key differences between anomaly detection and other forms of detection is that, rather than defining "what is not allowed or bad," it defines "what is allowed or good" Many traditional forms of detection rely on comparing observed behavior and noting when something known to be "bad" is seen These are often referred to as misuse detection systems, however, this nomenclature can be confusing since anomaly systems also detect misuse A more accurate name, perhaps, is explicit detection systems These systems operate well when the number of possible bad behaviors is small and does not change very rapidly However, in larger systems with greater variation, these two conditions often do not hold It then becomes a very onerous task to maintain the list of what is bad Anomaly detection relies on having some definition of allowed behavior and then noting when observed behaviors differ Often additional information about the deviation is noted including its nature, size and frequency in order to further understand what the anomaly is This operates well when it is easier or more efficient to define what is allowed rather than what is not allowed In these cases, the definition of what is allowed tends to be much shorter It also tends not to require changes as new problems are created or discovered Naturally, however, some problems are more applicable than others to this type of approach In particular, certain considerations may alter the effectiveness of anomaly detection for a specific problem Since it is detecting things in a more general way, an anomaly detection system may not be able to identify a problem as precisely as an explicit system It is also important to consider the space that is being observed for anomalies If it is too large, variant and complex, it may be too difficult to describe problems in a manner general enough to make anomaly detection easible (eg, general human behavior) Fortunately, many computer and network problems tend not to fall into this category which is what makes anomaly detection such an attractive technique Anomaly detection systems monitor networks for two primary criteria, characteristic deviation and statistical deviation While it can certainly be argued that there are more (or less) ways to classify such systems, these two divisions provide a convenient manner in which to discuss the various implementations Characteristic deviations tend to be more qualitative For example, "User joe123 does not normally use transfer files outside of the company" Statistical deviations, on the other hand, tend to be more quantitative For example, "This site's ICMP traffic never exceeds 15 percent of capacity".

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