Network Anomaly Intrusion Detection Based on Deep Learning Approach
The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2171 |
| _version_ | 1797618211284320256 |
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| author | Yung-Chung Wang Yi-Chun Houng Han-Xuan Chen Shu-Ming Tseng |
| author_facet | Yung-Chung Wang Yi-Chun Houng Han-Xuan Chen Shu-Ming Tseng |
| author_sort | Yung-Chung Wang |
| collection | DOAJ |
| description | The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device. |
| first_indexed | 2024-03-11T08:10:58Z |
| format | Article |
| id | doaj.art-82794ad6646a41bea45022b28ec61a9c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:58Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-82794ad6646a41bea45022b28ec61a9c2023-11-16T23:11:15ZengMDPI AGSensors1424-82202023-02-01234217110.3390/s23042171Network Anomaly Intrusion Detection Based on Deep Learning ApproachYung-Chung Wang0Yi-Chun Houng1Han-Xuan Chen2Shu-Ming Tseng3Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 106, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 106, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei 106, TaiwanThe prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models—deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM—were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.https://www.mdpi.com/1424-8220/23/4/2171deep learningnetwork intrusion detectiondata processing |
| spellingShingle | Yung-Chung Wang Yi-Chun Houng Han-Xuan Chen Shu-Ming Tseng Network Anomaly Intrusion Detection Based on Deep Learning Approach Sensors deep learning network intrusion detection data processing |
| title | Network Anomaly Intrusion Detection Based on Deep Learning Approach |
| title_full | Network Anomaly Intrusion Detection Based on Deep Learning Approach |
| title_fullStr | Network Anomaly Intrusion Detection Based on Deep Learning Approach |
| title_full_unstemmed | Network Anomaly Intrusion Detection Based on Deep Learning Approach |
| title_short | Network Anomaly Intrusion Detection Based on Deep Learning Approach |
| title_sort | network anomaly intrusion detection based on deep learning approach |
| topic | deep learning network intrusion detection data processing |
| url | https://www.mdpi.com/1424-8220/23/4/2171 |
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