Improving Transferability of Network Intrusion Detection in a Federated Learning Setup
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909064467841024 |
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| author | Ghosh, Shreya Jameel, Abu Shafin Mohammad Mahdee Gamal, Aly El |
| author_facet | Ghosh, Shreya Jameel, Abu Shafin Mohammad Mahdee Gamal, Aly El |
| contents | Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. A way to overcome this limitation is through transferable learning, where training for one intrusion class can lead to detection of unseen intrusion classes after deployment. In this paper, we provide a detailed study on the transferability of intrusion detection. We investigate practical federated learning configurations to enhance the transferability of intrusion detection. We propose two techniques to significantly improve the transferability of a federated intrusion detection system. The code for this work can be found at https://github.com/ghosh64/transferability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03560 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Transferability of Network Intrusion Detection in a Federated Learning Setup Ghosh, Shreya Jameel, Abu Shafin Mohammad Mahdee Gamal, Aly El Cryptography and Security Machine Learning Signal Processing Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. A way to overcome this limitation is through transferable learning, where training for one intrusion class can lead to detection of unseen intrusion classes after deployment. In this paper, we provide a detailed study on the transferability of intrusion detection. We investigate practical federated learning configurations to enhance the transferability of intrusion detection. We propose two techniques to significantly improve the transferability of a federated intrusion detection system. The code for this work can be found at https://github.com/ghosh64/transferability. |
| title | Improving Transferability of Network Intrusion Detection in a Federated Learning Setup |
| topic | Cryptography and Security Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2401.03560 |