A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments
The ever expanding communication requirements in today’s world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network envi...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/850153 |
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| author | Aneetha Avalappampatty Sivasamy Bose Sundan |
| author_facet | Aneetha Avalappampatty Sivasamy Bose Sundan |
| author_sort | Aneetha Avalappampatty Sivasamy |
| collection | DOAJ |
| description | The ever expanding communication requirements in today’s world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling’s T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling’s T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup’99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. |
| format | Article |
| id | doaj-art-f22061efb18b495f95141e0a039459a5 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f22061efb18b495f95141e0a039459a52025-02-03T01:23:40ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/850153850153A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network EnvironmentsAneetha Avalappampatty Sivasamy0Bose Sundan1Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, IndiaDepartment of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, IndiaThe ever expanding communication requirements in today’s world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling’s T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling’s T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup’99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.http://dx.doi.org/10.1155/2015/850153 |
| spellingShingle | Aneetha Avalappampatty Sivasamy Bose Sundan A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments The Scientific World Journal |
| title | A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments |
| title_full | A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments |
| title_fullStr | A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments |
| title_full_unstemmed | A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments |
| title_short | A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2 Statistics Approach for Network Environments |
| title_sort | dynamic intrusion detection system based on multivariate hotelling s t2 statistics approach for network environments |
| url | http://dx.doi.org/10.1155/2015/850153 |
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