Detecting Denial of Service attacks using machine learning algorithms
Abstract Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting the server's ability to provide resources to genuine customers, the affected server's resources, such as bandwidth and buffer size, are slowed down. A mathematical model for distrib...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-04-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-022-00616-0 |
| _version_ | 1818009917742645248 |
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| author | Kimmi Kumari M. Mrunalini |
| author_facet | Kimmi Kumari M. Mrunalini |
| author_sort | Kimmi Kumari |
| collection | DOAJ |
| description | Abstract Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting the server's ability to provide resources to genuine customers, the affected server's resources, such as bandwidth and buffer size, are slowed down. A mathematical model for distributed denial-of-service attacks is proposed in this study. Machine learning algorithms such as Logistic Regression and Naive Bayes, are used to detect attacks and normal scenarios. The CAIDA 2007 Dataset is used for experimental study. The machine learning algorithms are trained and tested using this dataset and the trained algorithms are validated. Weka data mining platform are used in this study for implementation and results of the same are analysed and compared. Other machine learning algorithms used with respect to denial of service attacks are compared with the existing work. |
| first_indexed | 2024-04-14T05:48:54Z |
| format | Article |
| id | doaj.art-b2d53e88401c4ad487ef4b41ae13d190 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-14T05:48:54Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-b2d53e88401c4ad487ef4b41ae13d1902022-12-22T02:09:12ZengSpringerOpenJournal of Big Data2196-11152022-04-019111710.1186/s40537-022-00616-0Detecting Denial of Service attacks using machine learning algorithmsKimmi Kumari0M. Mrunalini1M S Ramaiah Institute of TechnologyM S Ramaiah Institute of TechnologyAbstract Currently, Distributed Denial of Service Attacks are the most dangerous cyber danger. By inhibiting the server's ability to provide resources to genuine customers, the affected server's resources, such as bandwidth and buffer size, are slowed down. A mathematical model for distributed denial-of-service attacks is proposed in this study. Machine learning algorithms such as Logistic Regression and Naive Bayes, are used to detect attacks and normal scenarios. The CAIDA 2007 Dataset is used for experimental study. The machine learning algorithms are trained and tested using this dataset and the trained algorithms are validated. Weka data mining platform are used in this study for implementation and results of the same are analysed and compared. Other machine learning algorithms used with respect to denial of service attacks are compared with the existing work.https://doi.org/10.1186/s40537-022-00616-0DDOS attacksMachine learning for securityMathematical model for Bandwidth DepletionThroughput analysis of attack and normal scenario |
| spellingShingle | Kimmi Kumari M. Mrunalini Detecting Denial of Service attacks using machine learning algorithms Journal of Big Data DDOS attacks Machine learning for security Mathematical model for Bandwidth Depletion Throughput analysis of attack and normal scenario |
| title | Detecting Denial of Service attacks using machine learning algorithms |
| title_full | Detecting Denial of Service attacks using machine learning algorithms |
| title_fullStr | Detecting Denial of Service attacks using machine learning algorithms |
| title_full_unstemmed | Detecting Denial of Service attacks using machine learning algorithms |
| title_short | Detecting Denial of Service attacks using machine learning algorithms |
| title_sort | detecting denial of service attacks using machine learning algorithms |
| topic | DDOS attacks Machine learning for security Mathematical model for Bandwidth Depletion Throughput analysis of attack and normal scenario |
| url | https://doi.org/10.1186/s40537-022-00616-0 |
| work_keys_str_mv | AT kimmikumari detectingdenialofserviceattacksusingmachinelearningalgorithms AT mmrunalini detectingdenialofserviceattacksusingmachinelearningalgorithms |