LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks
Mobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimensio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/491910 |
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| _version_ | 1832555252043218944 |
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| author | Yuhua Zhang Kun Wang Min Gao Zhiyou Ouyang Siguang Chen |
| author_facet | Yuhua Zhang Kun Wang Min Gao Zhiyou Ouyang Siguang Chen |
| author_sort | Yuhua Zhang |
| collection | DOAJ |
| description | Mobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimension space, great pressure has been put on the subsequent data analysis and response phase. Therefore, traditional methods for intrusion detection can no longer be applicable in MSNs. To improve the performance of data analysis, we apply K -means algorithm to high-dimension data clustering analysis. Thus, an improved K -means clustering algorithm based on linear discriminant analysis (LDA) is proposed, called LKM algorithm. In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we use K -means algorithm for clustering analysis of the dimension-reduced data. Simulation results show that LKM algorithm shortens the sample feature extraction time and improves the accuracy of K -means clustering algorithm, both of which prove that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of IDS in MSNs. |
| format | Article |
| id | doaj-art-03f4f0c79d524dd7b854bc839b1c514a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-03f4f0c79d524dd7b854bc839b1c514a2025-02-03T05:48:36ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/491910491910LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor NetworksYuhua Zhang0Kun Wang1Min Gao2Zhiyou Ouyang3Siguang Chen4 Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China Electrical Engineering Department, UCLA, Los Angeles, CA 90095, USA Electrical Engineering Department, UCLA, Los Angeles, CA 90095, USA Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, ChinaMobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimension space, great pressure has been put on the subsequent data analysis and response phase. Therefore, traditional methods for intrusion detection can no longer be applicable in MSNs. To improve the performance of data analysis, we apply K -means algorithm to high-dimension data clustering analysis. Thus, an improved K -means clustering algorithm based on linear discriminant analysis (LDA) is proposed, called LKM algorithm. In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we use K -means algorithm for clustering analysis of the dimension-reduced data. Simulation results show that LKM algorithm shortens the sample feature extraction time and improves the accuracy of K -means clustering algorithm, both of which prove that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of IDS in MSNs.https://doi.org/10.1155/2015/491910 |
| spellingShingle | Yuhua Zhang Kun Wang Min Gao Zhiyou Ouyang Siguang Chen LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks International Journal of Distributed Sensor Networks |
| title | LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks |
| title_full | LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks |
| title_fullStr | LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks |
| title_full_unstemmed | LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks |
| title_short | LKM: A LDA-Based -Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks |
| title_sort | lkm a lda based means clustering algorithm for data analysis of intrusion detection in mobile sensor networks |
| url | https://doi.org/10.1155/2015/491910 |
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