LAN Intrusion Detection Using Convolutional Neural Networks
The world’s reliance the use of the internet is growing constantly, and data are considered the most precious parameter nowadays. It is critical to keep information secure from unauthorized people and organizations. When a network is compromised, information is taken. An intrusion detection system d...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6645 |
| _version_ | 1797480870778503168 |
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| author | Hanan Zainel Cemal Koçak |
| author_facet | Hanan Zainel Cemal Koçak |
| author_sort | Hanan Zainel |
| collection | DOAJ |
| description | The world’s reliance the use of the internet is growing constantly, and data are considered the most precious parameter nowadays. It is critical to keep information secure from unauthorized people and organizations. When a network is compromised, information is taken. An intrusion detection system detects both known and unexpected assaults that allow a network to be breached. In this research, we model an intrusion detection system trained to identify such attacks in LANs, and any computer network that uses data. We accomplish this by employing neural networks, a machine learning technique. We also investigate how well our model performs in multiclass categorization scenarios. On the NSL-KDD dataset, we investigate the performance of Convolutional Neural Networks such as CNN and CNN with LSTM. Our findings suggest that utilizing Convolutional Neural Networks to identify network intrusions is an effective strategy. |
| first_indexed | 2024-03-09T22:06:25Z |
| format | Article |
| id | doaj.art-479e43adcd0240afac0ea3cae3a1a441 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-479e43adcd0240afac0ea3cae3a1a4412023-11-23T19:40:19ZengMDPI AGApplied Sciences2076-34172022-06-011213664510.3390/app12136645LAN Intrusion Detection Using Convolutional Neural NetworksHanan Zainel0Cemal Koçak1Department of Computer Engineering, Gazi University, Ankara 06560, TurkeyDepartment of Computer Engineering, Gazi University, Ankara 06560, TurkeyThe world’s reliance the use of the internet is growing constantly, and data are considered the most precious parameter nowadays. It is critical to keep information secure from unauthorized people and organizations. When a network is compromised, information is taken. An intrusion detection system detects both known and unexpected assaults that allow a network to be breached. In this research, we model an intrusion detection system trained to identify such attacks in LANs, and any computer network that uses data. We accomplish this by employing neural networks, a machine learning technique. We also investigate how well our model performs in multiclass categorization scenarios. On the NSL-KDD dataset, we investigate the performance of Convolutional Neural Networks such as CNN and CNN with LSTM. Our findings suggest that utilizing Convolutional Neural Networks to identify network intrusions is an effective strategy.https://www.mdpi.com/2076-3417/12/13/6645intrusiondeep learningconvolutional neural networkattackmachine learning |
| spellingShingle | Hanan Zainel Cemal Koçak LAN Intrusion Detection Using Convolutional Neural Networks Applied Sciences intrusion deep learning convolutional neural network attack machine learning |
| title | LAN Intrusion Detection Using Convolutional Neural Networks |
| title_full | LAN Intrusion Detection Using Convolutional Neural Networks |
| title_fullStr | LAN Intrusion Detection Using Convolutional Neural Networks |
| title_full_unstemmed | LAN Intrusion Detection Using Convolutional Neural Networks |
| title_short | LAN Intrusion Detection Using Convolutional Neural Networks |
| title_sort | lan intrusion detection using convolutional neural networks |
| topic | intrusion deep learning convolutional neural network attack machine learning |
| url | https://www.mdpi.com/2076-3417/12/13/6645 |
| work_keys_str_mv | AT hananzainel lanintrusiondetectionusingconvolutionalneuralnetworks AT cemalkocak lanintrusiondetectionusingconvolutionalneuralnetworks |