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considered as normal use Hence, after applying the EWMA control chart to the testing data, each data observation in the testing data obtains a label of either attack or normal use The label of attack on any of the 300 data observations of text editing in the testing data produces a false alarm The label of attack on any of the attack data observations in the testing data gives a hit The detection performance of the EWMA control chart is measured by the total number of false alarms on all the normal use data observations in the testing data and the rst hit which is the observation number of the rst data observation in the attack data of the testing data which is labeled as attack The description of the false alarms and the rst hit can be found in 13 Similarly, an EWMA control chart is developed for the same variable, x, but with the normal use activity of web browsing, using the rst half of the 10-minute web browsing data from Run 3 as the training data This EWMA control chart is then tested on the second half of the 10-minute web browsing data and the ARP attack data from Run 3 for the variable The detection performance measures of the false alarms and the rst hit are obtained Hence, for each attack characteristic in Table 131 which involves a given attack and a given data variable, two EWMA control charts are developed for two normal use activities of text editing and web browsing in combination with the attack The detection performance measures of each EWMA control chart are obtained Table 141 shows the false alarms of each EWMA control chart for each attack characteristic in Table 131 Table 142 shows the rst hit of each EWMA control chart for each attack characteristic For each variable in each combination of an attack activity and a normal use activity in Tables 141 and 142, the false alarms and the rst hit of the EWMA control chart are either worse or the same as those of the cuscore-based attack norm separation model Table 143, which is the same as Table 134, compares the detection performance of the EWMA control charts with that of the ANN-based signature recognition models (or simply the ANN models) and that of the cuscore-based attack norm separation models (or simply the cuscore models) As shown in Table 143, for each normal use activity in combination with each attack, the EWMA control charts are worse than the cuscore models in both the false alarm and the rst hit The EWMA control charts produce 1,023 false alarms in total for all the combinations of the attack activities and the normal use activities, which are 1001 more false alarms than 22 false alarms in total produced by the cuscore models for all the combinations of the attacks and the normal use activities The EWMA control charts have 3761 observations of detection delay, which are computed by subtracting the total number of the attack-norm combinations (= 22) from the total number of the rst hits for all the attack-norm combinations (= 3783), whereas the cuscore models have 1035 observations of detection delays For the text editing activity in combination with the attacks, the EWMA control charts produce fewer false alarms than the ANN models for all the 11 attacks, and give an earlier detection than the ANN models for nine out of the 11 attacks For the web browsing activity in combination with the attacks, the EWMA control charts produce fewer false alarms than the ANN models for ten attacks, and give an earlier detection than the ANN models for seven attacks Hence, for those variables in Table 131, the EMWA control charts produce the worse detection performance than the cuscore models Like the ANN-based signature recognition technique described in 13, the EWMA control chart based anomaly detection technique has a similar drawback in lack of handling the mixed attack and normal use data and subtle data features This drawback may lead to the worse performance of the WMA control charts. crystal reports barcode not showing Barcode does not display in Crystal Reports ActiveX Viewer on the ... 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