Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges
In order to provide an accurate and timely response to different types of the attacks, intrusion and anomaly detection systems collect and analyze a lot of data that may include personal and other sensitive data. These systems could be considered a source of privacy-aware risks. Application of the f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/7/247 |
| _version_ | 1797433977287475200 |
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| author | Elena Fedorchenko Evgenia Novikova Anton Shulepov |
| author_facet | Elena Fedorchenko Evgenia Novikova Anton Shulepov |
| author_sort | Elena Fedorchenko |
| collection | DOAJ |
| description | In order to provide an accurate and timely response to different types of the attacks, intrusion and anomaly detection systems collect and analyze a lot of data that may include personal and other sensitive data. These systems could be considered a source of privacy-aware risks. Application of the federated learning paradigm for training attack and anomaly detection models may significantly decrease such risks as the data generated locally are not transferred to any party, and training is performed mainly locally on data sources. Another benefit of the usage of federated learning for intrusion detection is its ability to support collaboration between entities that could not share their dataset for confidential or other reasons. While this approach is able to overcome the aforementioned challenges it is rather new and not well-researched. The challenges and research questions appear while using it to implement analytical systems. In this paper, the authors review existing solutions for intrusion and anomaly detection based on the federated learning, and study their advantages as well as open challenges still facing them. The paper analyzes the architecture of the proposed intrusion detection systems and the approaches used to model data partition across the clients. The paper ends with discussion and formulation of the open challenges. |
| first_indexed | 2024-03-09T10:24:40Z |
| format | Article |
| id | doaj.art-c89c8e40585042d0951b9203666c83dd |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T10:24:40Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-c89c8e40585042d0951b9203666c83dd2023-12-01T21:48:06ZengMDPI AGAlgorithms1999-48932022-07-0115724710.3390/a15070247Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open ChallengesElena Fedorchenko0Evgenia Novikova1Anton Shulepov2Saint Petersburg Institute for Informatics and Automation, Federal Research Center of the Russian Academy of Sciences, 199178 Saint Petersburg, RussiaSaint Petersburg Institute for Informatics and Automation, Federal Research Center of the Russian Academy of Sciences, 199178 Saint Petersburg, RussiaSaint Petersburg Institute for Informatics and Automation, Federal Research Center of the Russian Academy of Sciences, 199178 Saint Petersburg, RussiaIn order to provide an accurate and timely response to different types of the attacks, intrusion and anomaly detection systems collect and analyze a lot of data that may include personal and other sensitive data. These systems could be considered a source of privacy-aware risks. Application of the federated learning paradigm for training attack and anomaly detection models may significantly decrease such risks as the data generated locally are not transferred to any party, and training is performed mainly locally on data sources. Another benefit of the usage of federated learning for intrusion detection is its ability to support collaboration between entities that could not share their dataset for confidential or other reasons. While this approach is able to overcome the aforementioned challenges it is rather new and not well-researched. The challenges and research questions appear while using it to implement analytical systems. In this paper, the authors review existing solutions for intrusion and anomaly detection based on the federated learning, and study their advantages as well as open challenges still facing them. The paper analyzes the architecture of the proposed intrusion detection systems and the approaches used to model data partition across the clients. The paper ends with discussion and formulation of the open challenges.https://www.mdpi.com/1999-4893/15/7/247artificial intelligencedata partitionfederated learningInternet of Thingsintrusion detectionmachine learning |
| spellingShingle | Elena Fedorchenko Evgenia Novikova Anton Shulepov Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges Algorithms artificial intelligence data partition federated learning Internet of Things intrusion detection machine learning |
| title | Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges |
| title_full | Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges |
| title_fullStr | Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges |
| title_full_unstemmed | Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges |
| title_short | Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges |
| title_sort | comparative review of the intrusion detection systems based on federated learning advantages and open challenges |
| topic | artificial intelligence data partition federated learning Internet of Things intrusion detection machine learning |
| url | https://www.mdpi.com/1999-4893/15/7/247 |
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