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FIGURE 15. KNN (K = 1) Confusion matrix for NSL-KDD 41.

Figure 15 KNN (K = 1) Confusion matrix for NSL-KDD 41.

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Eq.(7) _Tepresents the average fuzzy similarity value between Xp, Xz. The parameter ‘of’ used in eq.(7) is the standard deviation value which can be obtained by applying Eq.(8). ihe distance between Lany TWO 0 COnOMIONd! probvabdility vec- tors symbolized using Xp and x, can be obtained by using the proposed distance function whieh i is given by using equa- tion (6), with a = 0.3679.
Eq.(7) _Tepresents the average fuzzy similarity value between Xp, Xz. The parameter ‘of’ used in eq.(7) is the standard deviation value which can be obtained by applying Eq.(8). ihe distance between Lany TWO 0 COnOMIONd! probvabdility vec- tors symbolized using Xp and x, can be obtained by using the proposed distance function whieh i is given by using equa- tion (6), with a = 0.3679.
FIGURE 1. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using J48 classifier.
FIGURE 1. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using J48 classifier.
FIGURE 4. J48 Classifier Recall values before and after feature transformation using proposed approach for various classes. FIGURE 3. J48 Classifier Precision Values before and after feature transformation using proposed approach.
FIGURE 4. J48 Classifier Recall values before and after feature transformation using proposed approach for various classes. FIGURE 3. J48 Classifier Precision Values before and after feature transformation using proposed approach.
FIGURE 2. J48 Classifier accuracies before and after feature transformation using proposed approach and distance function.
FIGURE 2. J48 Classifier accuracies before and after feature transformation using proposed approach and distance function.
FIGURE 5. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using KNN (K = 1) classifier.
FIGURE 5. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using KNN (K = 1) classifier.
FIGURE 7. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using Naive Bayes classifier.
FIGURE 7. Confusion matrix obtained for KDD dataset with proposed feature transformation approach and using Naive Bayes classifier.
FIGURE 9. Percentage of Correctly classified instances for UTTAMA and PROPOSED methods.
FIGURE 9. Percentage of Correctly classified instances for UTTAMA and PROPOSED methods.
FIGURE 10. Percentage of Correctly classified instances for UTTAMA and PROPOSED methods.
FIGURE 10. Percentage of Correctly classified instances for UTTAMA and PROPOSED methods.
FIGURE 11. Accuracies for Feature transformation with GARUDA and PROPOSED distance function.
FIGURE 11. Accuracies for Feature transformation with GARUDA and PROPOSED distance function.
FIGURE 17. Accuracy, Precision and Recall values for KNN classifier using proposed feature transformation. FIGURE 16. Accuracy, Precision and Recall values for J48 classifier using proposed feature transformation.
FIGURE 17. Accuracy, Precision and Recall values for KNN classifier using proposed feature transformation. FIGURE 16. Accuracy, Precision and Recall values for J48 classifier using proposed feature transformation.
FIGURE 13. CANN [23] vs PROPOSED on KDD-19. FIGURE 12. UTTAMA vs PROPOSED on KDD-19.
FIGURE 13. CANN [23] vs PROPOSED on KDD-19. FIGURE 12. UTTAMA vs PROPOSED on KDD-19.
FIGURE 14. J48 Classifier confusion matrix for NSL-KDD 41.
FIGURE 14. J48 Classifier confusion matrix for NSL-KDD 41.
NUVVUSETTY RAJASEKHAR (Member, IEEE) received the Ph.D. degree from Acharya Nagar- juna University, India, in 2015, and the M.Tech. degree from Bharath University, Chennai, India. He is currently working as a Professor with the Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technol- ogy, Hyderabad, India. He has a total of 14 years of experience in teaching. He is actively involved in research and some of his research interests include image processing, network security, data mining, and artificial intelligence. He has published several research articles in International journals and conferences. He is also member of various professional organizations such as ACM and ISTE. He has received Best Paper Award at the IEEE International Conference, Agadir, Morocco, in 2016. He is also serving as a Reviewer and an Editorial Board Member for various reputed peer reviewed journals and technical committee member for various international conferences. UMA BOREGOWDA teceived the master’s degree from IIT Delhi, Delhi, India, and the Ph.D. degree from Mysore, India. She has nearly 25 years of teaching experience. She is currently the Professor and the Head of the Department of Information Science and Engineering, Malnad College of Engineering, Hassan. She is also rec- ognized with good administrative skills by the Malnad College of Engineering with honors and awards. She is also a member of CSI and ISTE. Her few areas of interests are data mining, multi core, and many-core architecture.
NUVVUSETTY RAJASEKHAR (Member, IEEE) received the Ph.D. degree from Acharya Nagar- juna University, India, in 2015, and the M.Tech. degree from Bharath University, Chennai, India. He is currently working as a Professor with the Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technol- ogy, Hyderabad, India. He has a total of 14 years of experience in teaching. He is actively involved in research and some of his research interests include image processing, network security, data mining, and artificial intelligence. He has published several research articles in International journals and conferences. He is also member of various professional organizations such as ACM and ISTE. He has received Best Paper Award at the IEEE International Conference, Agadir, Morocco, in 2016. He is also serving as a Reviewer and an Editorial Board Member for various reputed peer reviewed journals and technical committee member for various international conferences. UMA BOREGOWDA teceived the master’s degree from IIT Delhi, Delhi, India, and the Ph.D. degree from Mysore, India. She has nearly 25 years of teaching experience. She is currently the Professor and the Head of the Department of Information Science and Engineering, Malnad College of Engineering, Hassan. She is also rec- ognized with good administrative skills by the Malnad College of Engineering with honors and awards. She is also a member of CSI and ISTE. Her few areas of interests are data mining, multi core, and many-core architecture.
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