A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks
This document classifies, selects and trains a deep learning algorithm to create an IDS/IPS (Intrusion Prevention/Detection System) called Dique, which can detect and prevent denial of service (DoS) attacks. To mitigate DoS attacks, the IDS/IPS system, using the proposed deep learning model, classif...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9851436/ |
| _version_ | 1818483695853502464 |
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| author | Juan Fernando Canola Garcia Gabriel Enrique Taborda Blandon |
| author_facet | Juan Fernando Canola Garcia Gabriel Enrique Taborda Blandon |
| author_sort | Juan Fernando Canola Garcia |
| collection | DOAJ |
| description | This document classifies, selects and trains a deep learning algorithm to create an IDS/IPS (Intrusion Prevention/Detection System) called Dique, which can detect and prevent denial of service (DoS) attacks. To mitigate DoS attacks, the IDS/IPS system, using the proposed deep learning model, classifies incoming packets to the web server into two classes: benign (which are normal traffic packets) and malicious (which the system considers to contain possible DoS attacks). Dique has a Graphical User Interface (GUI) where “in real time” you can display graphically and textually the information of captured and classified packets, and allows you to switch between the IDS mode and the IPS mode of the system operation. The proposed DoS attack classification model uses a multi-layered Deep Feed Forward neural network, the CICDDoS2019 Dataset was used for training and an accuracy of 0.994 was achieved. In addition, an offensive system called Diluvio was developed to verify the functioning of the Dique system. In Diluvio seven different types of DoS attacks were implemented (five contents in the training Datset and two that are not in said dataset) that users can selectively launch against a web server. |
| first_indexed | 2024-12-10T15:45:20Z |
| format | Article |
| id | doaj.art-ed5e846ccce346758721b851e36e987c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T15:45:20Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ed5e846ccce346758721b851e36e987c2022-12-22T01:42:59ZengIEEEIEEE Access2169-35362022-01-0110830438306010.1109/ACCESS.2022.31966429851436A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service AttacksJuan Fernando Canola Garcia0https://orcid.org/0000-0002-1273-6651Gabriel Enrique Taborda Blandon1https://orcid.org/0000-0002-8067-1490Grupo Éxito S.A., Envigado, ColombiaResearch Group in Automation, Electronics and Computer Science, Instituto Tecnológico Metropolitano, Medellín, ColombiaThis document classifies, selects and trains a deep learning algorithm to create an IDS/IPS (Intrusion Prevention/Detection System) called Dique, which can detect and prevent denial of service (DoS) attacks. To mitigate DoS attacks, the IDS/IPS system, using the proposed deep learning model, classifies incoming packets to the web server into two classes: benign (which are normal traffic packets) and malicious (which the system considers to contain possible DoS attacks). Dique has a Graphical User Interface (GUI) where “in real time” you can display graphically and textually the information of captured and classified packets, and allows you to switch between the IDS mode and the IPS mode of the system operation. The proposed DoS attack classification model uses a multi-layered Deep Feed Forward neural network, the CICDDoS2019 Dataset was used for training and an accuracy of 0.994 was achieved. In addition, an offensive system called Diluvio was developed to verify the functioning of the Dique system. In Diluvio seven different types of DoS attacks were implemented (five contents in the training Datset and two that are not in said dataset) that users can selectively launch against a web server.https://ieeexplore.ieee.org/document/9851436/Denial of service attackdeep learningintrusion detection systemintrusion prevention systemneural networks |
| spellingShingle | Juan Fernando Canola Garcia Gabriel Enrique Taborda Blandon A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks IEEE Access Denial of service attack deep learning intrusion detection system intrusion prevention system neural networks |
| title | A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks |
| title_full | A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks |
| title_fullStr | A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks |
| title_full_unstemmed | A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks |
| title_short | A Deep Learning-Based Intrusion Detection and Preventation System for Detecting and Preventing Denial-of-Service Attacks |
| title_sort | deep learning based intrusion detection and preventation system for detecting and preventing denial of service attacks |
| topic | Denial of service attack deep learning intrusion detection system intrusion prevention system neural networks |
| url | https://ieeexplore.ieee.org/document/9851436/ |
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