CNN and MLP neural network ensembles for packet classification and adversary defense
Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions. Central to many of these research questions is the need to classify packets and improve visibility. Multi-Layer Perceptron (MLP) neural networks and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2021-03-01
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| Series: | Intelligent and Converged Networks |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2020.0023 |
| _version_ | 1797990725968723968 |
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| author | Bruce Hartpence Andres Kwasinski |
| author_facet | Bruce Hartpence Andres Kwasinski |
| author_sort | Bruce Hartpence |
| collection | DOAJ |
| description | Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions. Central to many of these research questions is the need to classify packets and improve visibility. Multi-Layer Perceptron (MLP) neural networks and Convolutional Neural Networks (CNNs) have been used to successfully identify individual packets. However, some datasets create instability in neural network models. Machine learning can also be subject to data injection and misclassification problems. In addition, when attempting to address complex communication network challenges, extremely high classification accuracy is required. Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models. After ensembles tuning, training time can be reduced, and a viable and effective architecture can be obtained. Because of their effectiveness, ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors. In this work, ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%. In addition, ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%. |
| first_indexed | 2024-04-11T08:41:16Z |
| format | Article |
| id | doaj.art-a1a9d132f46941549e933e347fe0ee7c |
| institution | Directory Open Access Journal |
| issn | 2708-6240 |
| language | English |
| last_indexed | 2024-04-11T08:41:16Z |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj.art-a1a9d132f46941549e933e347fe0ee7c2022-12-22T04:34:12ZengTsinghua University PressIntelligent and Converged Networks2708-62402021-03-0121668210.23919/ICN.2020.0023CNN and MLP neural network ensembles for packet classification and adversary defenseBruce Hartpence0Andres Kwasinski1<institution>GCCIS i-School at the Rochester Institute of Technology, Rochester</institution>, <city>New York</city>, <state>NY</state> <postal-code>14623</postal-code>, <country>USA</country><institution content-type="dept">Department of Computer Engineering</institution>, <institution>KGCOE at the Rochester Institute of Technology, Rochester</institution>, <city>New York</city>, <state>NY</state> <postal-code>14623</postal-code>, <country>USA</country>Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions. Central to many of these research questions is the need to classify packets and improve visibility. Multi-Layer Perceptron (MLP) neural networks and Convolutional Neural Networks (CNNs) have been used to successfully identify individual packets. However, some datasets create instability in neural network models. Machine learning can also be subject to data injection and misclassification problems. In addition, when attempting to address complex communication network challenges, extremely high classification accuracy is required. Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models. After ensembles tuning, training time can be reduced, and a viable and effective architecture can be obtained. Because of their effectiveness, ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors. In this work, ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%. In addition, ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.https://www.sciopen.com/article/10.23919/ICN.2020.0023convolutional neural network (cnn)multi-layer perception (mlp)ensembleclassificationadversary |
| spellingShingle | Bruce Hartpence Andres Kwasinski CNN and MLP neural network ensembles for packet classification and adversary defense Intelligent and Converged Networks convolutional neural network (cnn) multi-layer perception (mlp) ensemble classification adversary |
| title | CNN and MLP neural network ensembles for packet classification and adversary defense |
| title_full | CNN and MLP neural network ensembles for packet classification and adversary defense |
| title_fullStr | CNN and MLP neural network ensembles for packet classification and adversary defense |
| title_full_unstemmed | CNN and MLP neural network ensembles for packet classification and adversary defense |
| title_short | CNN and MLP neural network ensembles for packet classification and adversary defense |
| title_sort | cnn and mlp neural network ensembles for packet classification and adversary defense |
| topic | convolutional neural network (cnn) multi-layer perception (mlp) ensemble classification adversary |
| url | https://www.sciopen.com/article/10.23919/ICN.2020.0023 |
| work_keys_str_mv | AT brucehartpence cnnandmlpneuralnetworkensemblesforpacketclassificationandadversarydefense AT andreskwasinski cnnandmlpneuralnetworkensemblesforpacketclassificationandadversarydefense |