Statistical anomaly detection in .NET Maker Code 128 in .NET Statistical anomaly detection Statistical anomaly detectionUSS Code 128 Decoder In .NET FrameworkUsing Barcode Control SDK for VS .NET Control to generate, create, read, scan barcode image in .NET applications.For a sequence of uncorrelated data observations, x(n), n = 1, , N , with the mean of x and the standard deviation of x , the EWMA control chart rst performs the EWMA data smoothing as follows [1, 2, 4 6]: z (n) = x (n) + (1 ) z (n 1) , 0 < 1 (141)ANSI/AIM Code 128 Creation In VS .NETUsing Barcode creator for Visual Studio .NET Control to generate, create Code 128 image in VS .NET applications.The smoothed data, z(n), has approximately a normal distribution with the following mean and standard deviation: z = x z = x 2 (142) (143)Reading ANSI/AIM Code 128 In .NETUsing Barcode decoder for .NET framework Control to read, scan read, scan image in VS .NET applications.The EWMA control chart then monitors the smoothed data sequence, z(n), n = 1, , N If z(n) falls outside the range de ned by the Lower Control Limit (LCL) and Upper Control Limit (UCL), [LCLz , UCLz ], LCLz = z L z UCLz = z + L z , (144) (145)Painting Barcode In Visual Studio .NETUsing Barcode creation for .NET framework Control to generate, create bar code image in .NET applications.an anomaly is detected to signal an alarm for an attack The parameter, L, is de ned according to the desired Type-I error or false alarm rate For example, L is 196 for the 005 signi cance level of type-I error The EWMA control chart for time series data with autocorrelated data observations [1, 2, 4 6] monitors the prediction error, e(n), n = 1, , N , instead of the smoothed data, z(n) At rst, z(n 1), which is computed using Formula 141, is considered the one-step-ahead prediction of x(n) The prediction error for x(n) is the following: e (n) = x (n) z (n 1) (146)Bar Code Decoder In .NET FrameworkUsing Barcode reader for .NET Control to read, scan read, scan image in .NET framework applications.The prediction error data, e(n), n = 1, , N, is approximately independently and normally distributed with the mean of e = 0 and the standard deviation of e The estimate of e can be obtained as follows: e2 (n) = e2 (n) + (1 ) e2 (n 1) , 0 < 1 LCLe and UCLe of e(n) are de ned as follows: LCLe (n) = e L e (n 1) = L e (n 1) UCLe (n) = e + L e (n 1) = +L e (n 1) (148) (149) (147)Code128 Printer In C#Using Barcode generator for .NET Control to generate, create Code 128 image in VS .NET applications.Application of the ewma control chart to cyber attack detection Code 128 Code Set B Maker In Visual Studio .NETUsing Barcode creator for ASP.NET Control to generate, create Code 128C image in ASP.NET applications.Based on Formula 146, the EWMA control chart monitoring e(n) is equivalent to the EWMA control chart monitoring x(n) with the following control limits: LCLx (n) = z (n 1) L e (n 1) UCLx (n) = z (n 1) + L e (n 1) (1410) (1411)Code 128B Generation In VB.NETUsing Barcode generator for .NET Control to generate, create Code 128A image in Visual Studio .NET applications.142 APPLICATION OF THE EWMA CONTROL CHART TO CYBER ATTACK DETECTION Generate Bar Code In Visual Studio .NETUsing Barcode printer for VS .NET Control to generate, create bar code image in Visual Studio .NET applications.The Windows performance objects data described in 7 is used to test the application of the EWMA control charts to cyber attack detection The testing of the EWMA control charts on other data sets can be found in [5, 6] As described in 10, many data variables of the Windows performance objects have a certain degree of autocorrelation Hence, the EWMA control chart for autocorrelated data is applied to the Windows performance object data to detect the eleven attacks described in 7 In order to compare the detection performance of the EWMA control charts in this chapter, the ANN-based signature recognition models described in 13 and the cuscore-based attack norm separation models described in 17, the three techniques are tested using the data of the same variables which are involved in the attack characteristics in Table 131 Speci cally, two EWMA control charts are developed for each attack characteristic in Table 131 for the attack with that attack characteristic in combination with the text editing norm and the web browsing norm, respectively, using Formulas 141, 146, 147, 1410 and 1411 For example, the ARP Poison attack has the autocorrelation increase characteristic in Network Interface\Bytes Received/sec This data variable, Network Interface\Bytes Received/sec, is x in Formulas 141 and 146 As described in 7, Run 2 of the data collection for the ARP Poison attack contains the 10 minutes of the text editing data followed by the mixture of the text editing data and the ARP Poison attack data, and Run 3 of the data collection contains the 10-minute web browsing data followed by the mixture of the text editing data and the ARP Poison attack data Two EWMA control charts are developed, one for each of the two normal use activities: text editing and web browsing For the normal use activity of text editing, the rst half of the 10-minute text editing data from Run 2, which contains time series data of 300 data observations for the variable, x, is used as the training data The second half of the 10-minute text editing data and the ARP attack data from Run 2 for the variable, x, is used as the testing data Since the EWMA control chart is an anomaly detection technique and does not require the attack data for the training phase, the attack data from Run 1 of the data collection is not used to develop the EWMA control chart In the training and the testing, both and are set to 03, and L is set to 3, according to work in [5, 6] In the training phase of developing the EWMA control chart, z(0) is initialized to the average of the x values in the training data For each x(n) in the training data, Formula 141 is used to compute z(n), and Formula 146 is then used to compute e(n) At the beginning of the testing phase, z(0) is initialized to the average of z s computed from the training data, and e2 (0) is initialized to the average of e2 s from the training data For x(n) in the testing data from n = 1 to the last data observation, LCLx (n) and UCLx (n) are computed using Formulas 1410 and 1411 after computing z(n 1) using Formula 141 and e (n 1) using Formulas 146 and 147 If x(n) falls outside [LCLx (n, UCLx (n)], x(n) is considered as attack; otherwise, x(n) isPrint Bar Code In .NET FrameworkUsing Barcode creator for .NET framework Control to generate, create bar code image in .NET applications.Table 141 False alarms of EWMA control charts for attacks with the text editing (T) and web browsing (W) normsDraw EAN13 In .NETUsing Barcode drawer for .NET framework Control to generate, create European Article Number 13 image in .NET framework applications.Attacks Apache T 14 1 3 11 12 14 12 2 2 3 6 1 1 5 14 14 13 3 0 34 23 43 23 14 32 3 14 4 20 15 12 5 12 16 0 2 1 3 2 5 2 21 21 34 34 2 2 0 0 1 0 2 18 2 15 12 7 1 13 5 0 10 2 16 3 1 2 3 W T W T W T W T W T W T W T W T ARP Distributed Fork FTP Hardware Remote Rootkit Security W Software Vulnerability T W T WGenerate Leitcode In .NETUsing Barcode maker for .NET framework Control to generate, create Leitcode image in .NET framework applications.Barcode Maker In .NET FrameworkUsing Barcode drawer for ASP.NET Control to generate, create bar code image in ASP.NET applications.Encoding Code 39 Extended In C#Using Barcode creator for Visual Studio .NET Control to generate, create Code39 image in VS .NET applications.Bar Code Printer In Visual Basic .NETUsing Barcode generation for VS .NET Control to generate, create bar code image in .NET framework applications.Encode Barcode In C#.NETUsing Barcode creator for .NET framework Control to generate, create bar code image in .NET framework applications.