Applying Neural Network to U2R Attacks
Intrusion detection using artificial neural networks is an ongoing area and thus interest in this field has increased among the researchers. Therefore, in this paper we present a system for tackling User to Root (U2R) attacks using generalized feedforward neural network. A backpropagation algorithm...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2010
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/3081/ http://scholars.utp.edu.my/id/eprint/3081/1/6.pdf |
| _version_ | 1848659348355547136 |
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| author | Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi |
| author_facet | Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi |
| author_sort | Iftikhar , Ahmad |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | Intrusion detection using artificial neural networks is an ongoing area and thus interest in this field has increased among the researchers. Therefore, in this paper we present a system for tackling User to Root (U2R) attacks using generalized feedforward neural network. A backpropagation algorithm is used for training and testing purpose. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The system is implemented in two phases such as training phase and testing phase. The developed system is applied to different U2R attacks to test its performance. Furthermore, the results indicate that this approach is more precise and accurate in case of false positive, false negative and detection rate.
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| first_indexed | 2025-11-13T07:29:00Z |
| format | Conference or Workshop Item |
| id | oai:scholars.utp.edu.my:3081 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:29:00Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:30812010-11-12T01:19:42Z http://scholars.utp.edu.my/id/eprint/3081/ Applying Neural Network to U2R Attacks Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi QA75 Electronic computers. Computer science Intrusion detection using artificial neural networks is an ongoing area and thus interest in this field has increased among the researchers. Therefore, in this paper we present a system for tackling User to Root (U2R) attacks using generalized feedforward neural network. A backpropagation algorithm is used for training and testing purpose. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The system is implemented in two phases such as training phase and testing phase. The developed system is applied to different U2R attacks to test its performance. Furthermore, the results indicate that this approach is more precise and accurate in case of false positive, false negative and detection rate. 2010 Conference or Workshop Item NonPeerReviewed application/zip en http://scholars.utp.edu.my/id/eprint/3081/1/6.pdf Iftikhar , Ahmad and Azween, Abdullah and Abdullah , S. Alghamdi (2010) Applying Neural Network to U2R Attacks. In: 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010) , 3-6/10, Penang, Malaysia. |
| spellingShingle | QA75 Electronic computers. Computer science Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi Applying Neural Network to U2R Attacks |
| title | Applying Neural Network to U2R Attacks |
| title_full | Applying Neural Network to U2R Attacks |
| title_fullStr | Applying Neural Network to U2R Attacks |
| title_full_unstemmed | Applying Neural Network to U2R Attacks |
| title_short | Applying Neural Network to U2R Attacks |
| title_sort | applying neural network to u2r attacks |
| topic | QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/3081/ http://scholars.utp.edu.my/id/eprint/3081/1/6.pdf |