Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6671 |
| _version_ | 1797597917426483200 |
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| author | Martin Chovanec Martin Hasin Martin Havrilla Eva Chovancová |
| author_facet | Martin Chovanec Martin Hasin Martin Havrilla Eva Chovancová |
| author_sort | Martin Chovanec |
| collection | DOAJ |
| description | This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives. |
| first_indexed | 2024-03-11T03:12:03Z |
| format | Article |
| id | doaj.art-f2990bca67d7445e9bc4f3e6656afbb1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:12:03Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f2990bca67d7445e9bc4f3e6656afbb12023-11-18T07:35:12ZengMDPI AGApplied Sciences2076-34172023-05-011311667110.3390/app13116671Detection of HTTP DDoS Attacks Using NFStream and TensorFlowMartin Chovanec0Martin Hasin1Martin Havrilla2Eva Chovancová3Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, SlovakiaDepartment of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, SlovakiaDepartment of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, SlovakiaDepartment of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, SlovakiaThis paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.https://www.mdpi.com/2076-3417/13/11/6671TensorFlowNFStreammachine learningHTTP DDoS |
| spellingShingle | Martin Chovanec Martin Hasin Martin Havrilla Eva Chovancová Detection of HTTP DDoS Attacks Using NFStream and TensorFlow Applied Sciences TensorFlow NFStream machine learning HTTP DDoS |
| title | Detection of HTTP DDoS Attacks Using NFStream and TensorFlow |
| title_full | Detection of HTTP DDoS Attacks Using NFStream and TensorFlow |
| title_fullStr | Detection of HTTP DDoS Attacks Using NFStream and TensorFlow |
| title_full_unstemmed | Detection of HTTP DDoS Attacks Using NFStream and TensorFlow |
| title_short | Detection of HTTP DDoS Attacks Using NFStream and TensorFlow |
| title_sort | detection of http ddos attacks using nfstream and tensorflow |
| topic | TensorFlow NFStream machine learning HTTP DDoS |
| url | https://www.mdpi.com/2076-3417/13/11/6671 |
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