Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms
Clustering or (cluster analysis ) has been widely used in data analysis and pattern recognition. There are several algorithms for clustering large data sets or streaming data sets, Their aims to organize a collection of data items into clusters. These such items are more similar to each other within...
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Mosul University
2012-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
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| Online Access: | https://csmj.mosuljournals.com/article_163706_1bac5fa4f7a6ea037b91acf1d55662bd.pdf |
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| author | Manar Kashmola Bayda Khaleel |
| author_facet | Manar Kashmola Bayda Khaleel |
| author_sort | Manar Kashmola |
| collection | DOAJ |
| description | Clustering or (cluster analysis ) has been widely used in data analysis and pattern recognition. There are several algorithms for clustering large data sets or streaming data sets, Their aims to organize a collection of data items into clusters. These such items are more similar to each other within cluster, and difference than they are in the other clusters. Three fuzzy clustering algorithms (Fuzzy C-Means, Possibilistic C-Means and Gustafson-Kessel algorithms) were applied using kdd cup 99 data set to classify this data set into 23 classes according to the subtype of attacks. The same data set were classified into 5 classes according to the type of attacks. In order to evaluate the performance of the system, we compute the classification rate, detection rate and false alarm rate on this data set. Finally, the results obtained from the experiments with classification rate 100% which has not been obtained in any previous work. |
| format | Article |
| id | doaj-art-c2aff0fd53544cdbad9eb354653dd220 |
| institution | OA Journals |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2012-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-c2aff0fd53544cdbad9eb354653dd2202025-08-20T02:13:24ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902012-12-019212513810.33899/csmj.2012.163706163706Clustering and Detecting Network Intrusion Based on Fuzzy AlgorithmsManar Kashmola0Bayda Khaleel1College of Computer Sciences and Mathematics University of MosulCollege of Computer Sciences and Mathematics University of MosulClustering or (cluster analysis ) has been widely used in data analysis and pattern recognition. There are several algorithms for clustering large data sets or streaming data sets, Their aims to organize a collection of data items into clusters. These such items are more similar to each other within cluster, and difference than they are in the other clusters. Three fuzzy clustering algorithms (Fuzzy C-Means, Possibilistic C-Means and Gustafson-Kessel algorithms) were applied using kdd cup 99 data set to classify this data set into 23 classes according to the subtype of attacks. The same data set were classified into 5 classes according to the type of attacks. In order to evaluate the performance of the system, we compute the classification rate, detection rate and false alarm rate on this data set. Finally, the results obtained from the experiments with classification rate 100% which has not been obtained in any previous work.https://csmj.mosuljournals.com/article_163706_1bac5fa4f7a6ea037b91acf1d55662bd.pdfnetwork intrusion detectionfuzzy c-means(fcm)possibilistic c-means(pcm) and gustafson-kessel (gk) algorithmskdd cup 99 data set |
| spellingShingle | Manar Kashmola Bayda Khaleel Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms Al-Rafidain Journal of Computer Sciences and Mathematics network intrusion detection fuzzy c-means(fcm) possibilistic c-means(pcm) and gustafson-kessel (gk) algorithms kdd cup 99 data set |
| title | Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms |
| title_full | Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms |
| title_fullStr | Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms |
| title_full_unstemmed | Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms |
| title_short | Clustering and Detecting Network Intrusion Based on Fuzzy Algorithms |
| title_sort | clustering and detecting network intrusion based on fuzzy algorithms |
| topic | network intrusion detection fuzzy c-means(fcm) possibilistic c-means(pcm) and gustafson-kessel (gk) algorithms kdd cup 99 data set |
| url | https://csmj.mosuljournals.com/article_163706_1bac5fa4f7a6ea037b91acf1d55662bd.pdf |
| work_keys_str_mv | AT manarkashmola clusteringanddetectingnetworkintrusionbasedonfuzzyalgorithms AT baydakhaleel clusteringanddetectingnetworkintrusionbasedonfuzzyalgorithms |