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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 feasible (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 oes 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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default As noted earlier, a full version of the free Personal Edition s on the book CD You will have to register this version at Borland's web site If you are a student or associated with an educational institution, Borland provides highly discounted prices for the Professional and Enterprise editions You can check Borland's website for details There are JBuilder project files for all the Java code examples found in this book on the CD as well Sun Forte Sun Microsystems distributes a Java IDE called Forte Forte is very similar to JBuilder in many ways While JBuilder seems to do better in reviews than Forte, there is a rapidly growing Forte community Sun provides two editions of Forte, a free community edition, and an enterprise edition The community edition has more features than JBuilder Personal For example, there is support for JDBC As would be expected, the enterprise edition has support for enterprise wide networked applications and other advanced features Forte's editor in some ways seems more advanced that JBuilder's It has macros, and is customizable even in the free version However, the JBuilder editor has more total commands, and comes with several predefined command sets On the other hand, the code generated by JBuilder's User Interface designer seems cleaner and easier to modify than Forte's In the end, the decision on which IDE, if any, to use is usually very personal In a large enough project, it is possible that a whole team will have to use the same IDE because of the support they give for version control and project coordination, but the important issue is to use the tool that maximizes your productivity.

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pipeline, performs the inverse Fourier transform, and outputs .The figures for this example show a simple Java framework for pipelines and an example application The framework consists of a base class for pipeline stages, PipelineStage, shown in Fig 427, and a base class for pipelines, LinearPipeline, shown in Fig 428 Applications provide a subclass of PipelineStage for each desired stage, implementing its three abstract methods to indicate what the stage should do on the initial step, the computation steps, and the final step, and a subclass of LinearPipeline that implements its abstract methods to create an array containing the desired pipeline stages and the desired queues connecting the stages For the queue connecting the stages, we use LinkedBlockingQueue, an implementation of the BlockingQueue interface These classes are found in the javautil concurrent package These classes use generics to specify the type of objects the queue can hold For example, new LinkedBlockingQueue<String> creates a BlockingQueue implemented by an underlying linked list that can hold Strings The operations of interest are put, to add an object to the queue, and take, to remove an object, take blocks if the queue is empty The class CountDownLatch, also found in the javautilconcurrent package, is a imple barrier that allows the program to print a message when it has terminated Barriers in general, and CountDownLatch in particular, are discussed in the Implementation Mechanisms design space The remaining figures show code for an example application, a pipeline to sort integers Fig 429 is the required subclass of LinearPipeline, and Fig 430 is the required subclass of PipelineStage Additional pipeline stages to generate or read the input and to handle the output are not shown. In .NET Framework Using Barcode printer for VS .Related: SSRS Barcode Generator SDK, Barcode Generator ASP.NET , Create Barcode Java