Detection performance of CCAS in .NET framework

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(b) Compare the session signal ratio of each session with the session signal threshold, and signal the session as attack if the session signal ratio is not smaller than the session signal threshold, or claim the session as normal use otherwise (c) Compute the false alarm rate as the ratio of the number of the normal use sessions which are signaled as attack (false alarms) to the total number of the normal use sessions, and the hit rate as the ratio of the number of the attack sessions which are signaled as attack (hits) to the total number of the attack sessions (d) Plot the pair of the false alarm rate and the hit rate in an ROC chart with the false alarm rate on the horizontal dimension and the hit rate on the vertical dimension (e) Get another session signal threshold by adding a small increment to the current session signal threshold (f) Repeat Steps (b) (e) until the session signal threshold is greater than the maximum session signal ratio of all the sessions in the testing data
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An ROC chart is a method of comparing the overall detection performance of two techniques without comparing a pair of false alarm rate and hit rate set results from different signal thresholds
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124 DETECTION PERFORMANCE OF CCAS
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Figures 121 124 show the ROC charts of the CCAS detection performance on the testing data for the CCAS applications to the four input orders of the training data respectively using both the dummy cluster method and the grid-based clustering method The closer an ROC curve of a given method is to the top-left corner of the ROC chart which represents the 100% hit rate and the 0% false alarm, the better detection performance the method produces If the ROC curve of method A rises completely above that of method B, the detection performance of method A is better than that of method B Hence, the ROC chart allows the performance evaluation of one method and the performance comparison of two or more methods independent of a speci c signal threshold selected for each method to produce the pair of the false alarm rate and the hit rate Figures 121, 122 and 124 for the rst, second and fourth input orders of the training data show that the dummy cluster-based CCAS produces a better detection performance than the grid-based CCAS by examining the ROC curves and the hit rates corresponding to the 0% false alarm rates in those ROC curves The dummy cluster-based CCAS produces the best performance for the fourth input order of the training data which have the attack sessions followed by the normal sessions For the third input order of the training data as shown in Figure 123, neither the dummy cluster CCAS nor the grid-based CCAS performs well To further reduce the impact of the input order of the training data points on the resulting clusters, the post-processing steps after the steps of CCAS described in Section 121 are added and described in [8] to improve the robustness of the clustering results to the input order of training data The distance measure in Formula 128 is applicable to only predictor variables that take numeric values The variations of CCAS to deal with both numeric and categorical predictor variables are described in [9, 10]
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Clustering and classifying attack and normal use data
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(a) 1 08 Hit rate 06 04 02 0 0 02 04 06 False alarm rate (b) 1 08 Hit rate 06 04 02 0 0 02 04 06 False alarm rate 08 1 05 S T D 06 S T D 07 S T D 08 1 05 S T D 06 S T D 07 S T D
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Figure 121 The ROC charts for the rst input order of the training data and the a values of 05, 06 and 07: (a) the dummy cluster method, and (b) the grid-based method From Figure 1 in [6], X Li, and N Ye, Grid- and dummy-cluster-based learning of normal and intrusive clusters for computer intrusion detection Quality and Reliability Engineering International, Vol 18, No 3, pp 231 242, 2002, c John Wiley & Sons Limited Reproduced with permission
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(a) 1 08 06 04 02 0 0 02 04 06 False alarm rate (b) 1 08 Hit rate 06 04 02 0 0 02 04 06 False alarm rate 08 1 05 S T D 06 S T D 07 S T D 08 1 05 S T D 06 S T D 07 S T D
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Figure 122 The ROC charts for the second input order of the training data and the a values of 05, 06 and 07: (a) the dummy cluster method, and (b) the grid-based method From Figure 2 in [6], X Li, and N Ye, Grid- and dummy-cluster-based learning of normal and intrusive clusters for computer intrusion detection Quality and Reliability Engineering International, Vol 18, No 3, pp 231 242, 2002, c John Wiley & Sons Limited Reproduced with permission
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