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of a given attack, are useful for detecting events of the attack Such data variables are identi ed for each of the eleven attacks in s 8 11 A feature is a easure of a property which exists in a sample of data observations for one or multiple data variables Only univariate mathematical/statistical features features of a data sample from one data variable are investigated in Part III These univariate mathematical/ statistical features include the statistical mean in 8, the probability distribution in 9, the autocorrelation in 10, and the wavelet-based signal strength in 11 covering the Haar wavelet, the Daubechies wavelet, the Derivative of Gaussian wavelet, the Paul wavelet and the Morlet wavelet These wavelets are used to extract the time-frequency signal changes associated with the data patterns of step change, steady change, random change, spike change and sine-cosine wave with noise s 8 11 provide mathematical/statistical methods of extracting the mean, probability distribution, autocorrelation, and wavelet features from attack and normal use data Among the four features, the distribution feature gives a more comprehensive picture of a data sample than the mean feature Both the wavelet feature and the autocorrelation feature reveal relations of data observations over time The autocorrelation feature focuses on the general autocorrelation aspect of time series data, whereas the wavelet feature focuses on special forms of time-frequency data patterns Both various wavelet forms and various probability distributions are linked to certain data patterns The distribution feature describes the general pattern of the data, whereas the wavelet feature reveals time locations and frequencies of special data patterns Hence, the wavelet feature reveals more special data features than the distribution feature and the autocorrelation feature Note that there are other types of univariate features (eg, features extracting other trends or patterns of data) as well as multivariate features (ie, features of data from multiple data variables) which are not investigated in Part III but may be useful in revealing data characteristics of various attacks If one or more events of a given attack cause a signi cant change in a speci c feature of a data variable, this change is considered a data characteristic of the attack s 8 11 describe statistical tests to identify a signi cant change in a given data feature, and reveal data characteristics of eleven attacks in the mean, probability distribution, autocorrelation, and wavelet features If a speci c data characteristic appears during a given attack but not during other attacks or normal use activities, this data characteristic is considered a unique data characteristic of that attack and can be used to uniquely detect and identify this attack Note that an event may manifest through more than one data characteristic (eg, more than one data variable or more than one feature of a data variable) The identi ed attack characteristics in the mean, distribution, autocorrelation and wavelet features are used to uncover the similarity and difference of the attacks The data characteristics of attack and normal use activities discovered in Part III are essential to building attack detection models for detection accuracy and earliness Attack detection models are covered in Parts IV VI.

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The Command pattern provides an excellent solution to such problems To implement it, you simply place each piece of request-handling logic in a separate "command" class that has a common method, like execute() or run(), for executing its encapsulated handler logic Once you have a family of such commands, you can use a collection to store and retrieve instances of them; add, remove, or change instances; and execute instances by invoking their execution methods Routing requests and executing diverse behavior in a uniform way may be so central to a design that you may find yourself using the Command pattern early, rather than refactoring to it later Many of the server-side, Web-based systems I've built have used the Command pattern to produce a standard way to route requests, execute actions, or forward actions to other actions The Example section shows how to refactor to such a solution The authors of Design Patterns [DP] explain how the Command pattern is often used to support an undo/redo capability A question that often arises in extreme programming (XP) circles is what to do when you aren't sure whether a system will need undo/redo Do you just implement the Command pattern in case the need arises Or is that a violation of "You aren't gonna need it," an XP principle that cautions against adding functionality to code based on speculation, not genuine need If I'm not sure whether a system needs the ommand pattern, I generally don't implement it, for I find that it isn't that hard to refactor to this pattern when the need arises However, if your code is getting into a state in which it will be harder and harder to refactor to the Command pattern and there's a good chance you'll soon need an undo/redo capability, it may make sense to refactor it to use Command before doing so will be impossibly hard It's a bit like taking out an insurance plan The Command pattern is easy to implement, versatile, and incredibly useful This refactoring captures only one area in which it is useful Because Command can solve other tricky problems, there could easily be additional refactorings to it.

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