A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection
The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large group...
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| Formaat: | Artikel |
| Taal: | English |
| Gepubliceerd in: |
MDPI AG
2022-08-01
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| Reeks: | Applied Sciences |
| Onderwerpen: | |
| Online toegang: | https://www.mdpi.com/2076-3417/12/16/7986 |
| _version_ | 1827623899514273792 |
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| author | Emad Ul Haq Qazi Abdulrazaq Almorjan Tanveer Zia |
| author_facet | Emad Ul Haq Qazi Abdulrazaq Almorjan Tanveer Zia |
| author_sort | Emad Ul Haq Qazi |
| collection | DOAJ |
| description | The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results. |
| first_indexed | 2024-03-09T11:57:09Z |
| format | Article |
| id | doaj.art-06c93b535f7445e9ab3a40ef8b28c73d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:57:09Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-06c93b535f7445e9ab3a40ef8b28c73d2023-11-30T23:07:10ZengMDPI AGApplied Sciences2076-34172022-08-011216798610.3390/app12167986A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion DetectionEmad Ul Haq Qazi0Abdulrazaq Almorjan1Tanveer Zia2Center of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 14812, Saudi ArabiaCenter of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 14812, Saudi ArabiaCenter of Excellence in Cybercrimes and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh 14812, Saudi ArabiaThe connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results.https://www.mdpi.com/2076-3417/12/16/7986network intrusion detection system (NIDS)CICIDS2017deep learningconvolutional neural network (CNN) |
| spellingShingle | Emad Ul Haq Qazi Abdulrazaq Almorjan Tanveer Zia A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection Applied Sciences network intrusion detection system (NIDS) CICIDS2017 deep learning convolutional neural network (CNN) |
| title | A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection |
| title_full | A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection |
| title_fullStr | A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection |
| title_full_unstemmed | A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection |
| title_short | A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection |
| title_sort | one dimensional convolutional neural network 1d cnn based deep learning system for network intrusion detection |
| topic | network intrusion detection system (NIDS) CICIDS2017 deep learning convolutional neural network (CNN) |
| url | https://www.mdpi.com/2076-3417/12/16/7986 |
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