Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks
Due to the rapid advancement of technologies including the tremendous growth of multimedia content, cloud computing and mobile usage, conventional networks are not able to meet the demands. Software-Defined Networks (SDN) are considered one of the key enabling technologies providing a new powerful n...
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| Format: | conferenceObject |
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2021
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| Online Access: | http://hdl.handle.net/10725/14334 https://doi.org/10.1109/MENACOMM50742.2021.9678279 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/9678279 |
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| _version_ | 1864513469256040448 |
|---|---|
| author | Abbas, Nadine |
| author2 | Nasser, Youssef Shehab, Maryam Sharafeddine, Sanaa |
| author2_role | author author author |
| author_facet | Abbas, Nadine Nasser, Youssef Shehab, Maryam Sharafeddine, Sanaa |
| author_role | author |
| dc.creator.none.fl_str_mv | Abbas, Nadine Nasser, Youssef Shehab, Maryam Sharafeddine, Sanaa |
| dc.date.none.fl_str_mv | 2021 2023-01-04T13:11:24Z 2023-01-04T13:11:24Z 2023-01-04 |
| dc.identifier.none.fl_str_mv | 9781665434447 http://hdl.handle.net/10725/14334 https://doi.org/10.1109/MENACOMM50742.2021.9678279 Abbas, N., Nasser, Y., Shehab, M., & Sharafeddine, S. (2021, December). Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks. In 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM) (pp. 142-146). IEEE. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/9678279 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | Due to the rapid advancement of technologies including the tremendous growth of multimedia content, cloud computing and mobile usage, conventional networks are not able to meet the demands. Software-Defined Networks (SDN) are considered one of the key enabling technologies providing a new powerful network architecture that allows the dynamic operation of different services using a common infrastructure. Despite their notable gains, SDNs may not be secure and are vulnerable to attacks. In this paper, we address the SDN vulnerabilities and present attack-specific feature selection to identify the features that have the most impact on anomaly detection. We first use the InSDN intrusion dataset that considers different attacks including Denial-of-Service (DoS), Distributed-DoS (DDoS), brute force, probe, web and botnet attacks. We then perform data pre-processing and apply univariate feature selection to select the features having the highest impact on the different attacks. These selected features can then be used to train the model which reduces the computational cost of modeling while keeping the high performance of the model. Detailed analysis and simulation results are then presented to show the predominant features and their impact on the different attacks. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_540c5f4a9dc8a46a0cb80923e9dcf35d |
| identifier_str_mv | 9781665434447 Abbas, N., Nasser, Y., Shehab, M., & Sharafeddine, S. (2021, December). Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks. In 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM) (pp. 142-146). IEEE. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/14334 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined NetworksAbbas, NadineNasser, YoussefShehab, MaryamSharafeddine, SanaaDue to the rapid advancement of technologies including the tremendous growth of multimedia content, cloud computing and mobile usage, conventional networks are not able to meet the demands. Software-Defined Networks (SDN) are considered one of the key enabling technologies providing a new powerful network architecture that allows the dynamic operation of different services using a common infrastructure. Despite their notable gains, SDNs may not be secure and are vulnerable to attacks. In this paper, we address the SDN vulnerabilities and present attack-specific feature selection to identify the features that have the most impact on anomaly detection. We first use the InSDN intrusion dataset that considers different attacks including Denial-of-Service (DoS), Distributed-DoS (DDoS), brute force, probe, web and botnet attacks. We then perform data pre-processing and apply univariate feature selection to select the features having the highest impact on the different attacks. These selected features can then be used to train the model which reduces the computational cost of modeling while keeping the high performance of the model. Detailed analysis and simulation results are then presented to show the predominant features and their impact on the different attacks.Includes bibliographical references.IEEE2023-01-04T13:11:24Z2023-01-04T13:11:24Z20212023-01-04Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject9781665434447http://hdl.handle.net/10725/14334https://doi.org/10.1109/MENACOMM50742.2021.9678279Abbas, N., Nasser, Y., Shehab, M., & Sharafeddine, S. (2021, December). Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks. In 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM) (pp. 142-146). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/9678279eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/143342023-01-04T13:11:24Z |
| spellingShingle | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks Abbas, Nadine |
| status_str | publishedVersion |
| title | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| title_full | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| title_fullStr | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| title_full_unstemmed | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| title_short | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| title_sort | Attack-Specific Feature Selection for Anomaly Detection in Software-Defined Networks |
| url | http://hdl.handle.net/10725/14334 https://doi.org/10.1109/MENACOMM50742.2021.9678279 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/9678279 |