winforms data matrix reader

winforms data matrix reader

winforms data matrix reader

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Before we can implement the code that retrieves the task groups we need to add the PieChart control to the Toolbox in Visual Studio and add a reference to the SharePoint Silverlight client object model assembly to our project Actually there are two ways to work with SharePoint data in Silverlight applications The first way is to use the SharePoint Silverlight Client Object model and the second way is to use SharePoint Soap and WCF data services In ur sample we will leverage the SharePoint Silverlight client object model To add the pie chart control to the toolbox, right click the Silverlight control bar in the toolbox and select Choose Items from the context menu Figure 9-33 shows the context menu. QR Code 2d Barcode Printer In Visual Basic .NET Using .Related: .NET WinForms Code 128 Generating , Generate EAN-13 VB.NET , ITF-14 Generator Excel

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) This is the same data used for testing in 12 Hence, a more detailed description of the data can be found in 12 The Mill data set has 68,871 normal use events in 14 normal use sessions and 36,036 attack data in seven attack sessions The Pascal data set has 81,755 normal use events in 63 normal use sessions and 32,327 attack events in four attack sessions Each audit event is considered as an observation of the computer system state at a given time The event type is considered to represent the system state There are 284 possible event types that can be recorded by BSM Hence, there are 284 possible system states However, only 69 different types of audit events appear in the Mill data set, and only 53 different types of audit events appear in the Pascal data set For the Mill data set, the normal events in the last hour of the three-hour Mill data set are used as the training data, and the entire set of the three-hour Mill data is used as the testing data There are 42,983 audit events in the Mill training data with 67 different event types For the Pascal data set, the normal events in the last hour of the three-hour Pascal data set are used as the training data, and the entire set of the three-hour Pascal data is used as the testing data There are 20,616 audit events in the Pascal training data with 53 different event types A Markov chain model, consisting of P and Q in Formulas 151 and 152, respectively, is built from the Mill training data using Formulas 153 and 154 For each audit event in the Mill testing data, this Markov chain model is then used to compute the probability of observing the short state sequence for this event in a window size of T under the normal use condition using Formula 155 Two T values of 10 events and 100 events are tested The ROC method is used to evaluate the detection performance of the Markov chain model-based anomaly detection technique by using a wide range of signal thresholds to obtain pairs of false alarm rate and hit rate For each signal threshold, if the probability of a given event in the Mill testing data is greater than this signal threshold, the event is considered attack; otherwise, the event is considered normal use A false alarm occurs when a normal use event in the testing data is considered attack A hit occurs when an attack event in the testing data is signaled as attack The false alarm rate and the hit rate are computed over all the events in the Mill testing data set Similarly, the Markov chain model-based anomaly detection technique is developed and tested using the Pascal training data and testing data Figure 151 shows the ROC charts of the Markov chain model based anomaly detection technique for the ill data set with the window sizes of 10 events and 100 events Figure 152.

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