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Fig.5 KNN accuracies for k=1, 3, 5 over NSL-KDD dataset with 19 attributes

Figure 5 KNN accuracies for k=1, 3, 5 over NSL-KDD dataset with 19 attributes

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Please cite this article as: G.R. Kumar, N. Mangathayaru, G. Narsimha, G.S. Reddy, A self This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. will undergo copyediting, typesetting, and review of the resulting proof before it is published in
Please cite this article as: G.R. Kumar, N. Mangathayaru, G. Narsimha, G.S. Reddy, A self This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. will undergo copyediting, typesetting, and review of the resulting proof before it is published in
Table -4: Generated clusters Final Mean and Standard deviation
Table -4: Generated clusters Final Mean and Standard deviation
vectors forms the input for the classifier again. For classifier, to judge the degree of
vectors forms the input for the classifier again. For classifier, to judge the degree of
Table -11: Classification for k=1
Table -11: Classification for k=1
Table -10: Similarities
Table -10: Similarities
Fig.2 shows the set of 41 attributes in NSL-KDD99 dataset.
Fig.2 shows the set of 41 attributes in NSL-KDD99 dataset.
Table 12: Confusion M atrix of KNN for threshold = 0.9999 and k=1
Table 12: Confusion M atrix of KNN for threshold = 0.9999 and k=1
Table 14: Confusion M atrix of KNN for threshold = 0.9999 and k=5
Table 14: Confusion M atrix of KNN for threshold = 0.9999 and k=5
Fig 3. KNN and J48 over NSL-KDD after Dimensionality reduction for similarity threshold, 0.9999 J48 classifier accuracies achieved by considering reduced dimension process matrix is given in Table 15. Fig.3 shows classification accuracies for various class labels using KNN and J48 classifiers with reduced dimensions adopting proposed approach of dimensionality reduction. It clearly shows that the U2R and R2L attacks have been identified more accurately. For KNN, U2R attack accuracy is 96% for k=5 and for J48 classifier it is 97%.
Fig 3. KNN and J48 over NSL-KDD after Dimensionality reduction for similarity threshold, 0.9999 J48 classifier accuracies achieved by considering reduced dimension process matrix is given in Table 15. Fig.3 shows classification accuracies for various class labels using KNN and J48 classifiers with reduced dimensions adopting proposed approach of dimensionality reduction. It clearly shows that the U2R and R2L attacks have been identified more accurately. For KNN, U2R attack accuracy is 96% for k=5 and for J48 classifier it is 97%.
Table 15: Confusion M atrix of 48 classifier for threshold = 0.9999 Tables 16, Table 17 and Table 18 shows the confusion matrix for KNN over NSL-KDL 99 with 41 attributes for k=1, k=3 and k=5 respectively.
Table 15: Confusion M atrix of 48 classifier for threshold = 0.9999 Tables 16, Table 17 and Table 18 shows the confusion matrix for KNN over NSL-KDL 99 with 41 attributes for k=1, k=3 and k=5 respectively.
Table 16 Confusion M atrix of KNN over NSL-KDD 99 with 41 attributes for k=1 Table 17 Confusion M atrix of KNN over NSL-KDD 99 with 41 attributes for k=3
Table 16 Confusion M atrix of KNN over NSL-KDD 99 with 41 attributes for k=1 Table 17 Confusion M atrix of KNN over NSL-KDD 99 with 41 attributes for k=3
Table 19 Confusion M atrix of 48 over NSL-KDD 99 with 41 attributes
Table 19 Confusion M atrix of 48 over NSL-KDD 99 with 41 attributes
-ig-4 R2L and U2R attack accuracies before and after dimensionality reduction - NSL KDD dataset
-ig-4 R2L and U2R attack accuracies before and after dimensionality reduction - NSL KDD dataset
Table 20: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=1
Table 20: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=1
Table 21: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=3 Table 21: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=<
Table 21: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=3 Table 21: Confusion M atrix of KNN over NSL-KDD 99 with 19 attributes for k=<
Table 23: Confusion M atrix of 48 over NSL-KDD 99 with 19 attributes
Table 23: Confusion M atrix of 48 over NSL-KDD 99 with 19 attributes
Figure 5 shows classification accuracies for all classes over NSL-KDD dataset obtained using KNN and J48 classifiers considering 19 attributes outlined in [32].
Figure 5 shows classification accuracies for all classes over NSL-KDD dataset obtained using KNN and J48 classifiers considering 19 attributes outlined in [32].
Fig.6 Comparison of U2R and R2L attack accuracies - KNN and J48 for 19 attributes
Fig.6 Comparison of U2R and R2L attack accuracies - KNN and J48 for 19 attributes
ig-7 Comparison of accuracies obtained using KNN classifier for 41, 19 and 35 dimension: dimensional reduction is better compared to 41 and 19 dimensions.
ig-7 Comparison of accuracies obtained using KNN classifier for 41, 19 and 35 dimension: dimensional reduction is better compared to 41 and 19 dimensions.
Fig.8 Comparison of accuracies obtained using proposed dimensionality reduction with kKNN and CANN Gaussian approach KNN accurcaies over NSL-KDD dataset with CANN-Gaussian and Our approach
Fig.8 Comparison of accuracies obtained using proposed dimensionality reduction with kKNN and CANN Gaussian approach KNN accurcaies over NSL-KDD dataset with CANN-Gaussian and Our approach
Fig.9 Proposed vs. CANN using Gaussian measure
Fig.9 Proposed vs. CANN using Gaussian measure
Fig 10 ROC Curves for DARPA before and after dimensionality reduction considering binary process representation
Fig 10 ROC Curves for DARPA before and after dimensionality reduction considering binary process representation
Fig 11 ROC Curves for DARPA before and after dimensionality reduction considering frequency
Fig 11 ROC Curves for DARPA before and after dimensionality reduction considering frequency
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