Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset
This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation fu...
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| Vydané v: | Proceedings (IEEE International Symposium on Embedded Multicore/Manycore SoCs. Online) s. 251 - 255 |
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| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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IEEE
01.12.2022
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| ISSN: | 2771-3075 |
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| Abstract | This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems. |
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| AbstractList | This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems. |
| Author | Tanimu, Jesse Jeremiah Robert, Patience Hamada, Mohamed Mahendran, Anand |
| Author_xml | – sequence: 1 givenname: Jesse Jeremiah surname: Tanimu fullname: Tanimu, Jesse Jeremiah email: tanimujessej@gmail.com organization: Bayero University, Kano,Dept. Of Computer Science,Kano,Nigeria – sequence: 2 givenname: Mohamed surname: Hamada fullname: Hamada, Mohamed email: hamada@u-aizu.ac.jp organization: University of Aizu,Software Engineering Lab.,Aizu,Japan – sequence: 3 givenname: Patience surname: Robert fullname: Robert, Patience email: robertpatience44@gmail.com organization: Federal Polytechnic, Bali,Department of Computer Science,Bali,Nigeria – sequence: 4 givenname: Anand surname: Mahendran fullname: Mahendran, Anand email: amahendran@hse.ru organization: Higher School of Economics,Theoretical Computer Science Lab.,Moscow Moscow,Russia |
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| PublicationTitle | Proceedings (IEEE International Symposium on Embedded Multicore/Manycore SoCs. Online) |
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| Snippet | This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in... |
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| SubjectTerms | Activation function Autoencoder Deep learning Intrusion detection Multicore processing Network intrusion detection Regularization Softmax and Sparse Autoencoder |
| Title | Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset |
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