Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913287704150016 |
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| author | Hesford, Jake Cheng, Daniel Wan, Alan Huynh, Larry Kim, Seungho Kim, Hyoungshick Hong, Jin B. |
| author_facet | Hesford, Jake Cheng, Daniel Wan, Alan Huynh, Larry Kim, Seungho Kim, Hyoungshick Hong, Jin B. |
| contents | Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset. For example, BoT_IoT and Stratosphere IoT datasets both capture IoT-related attacks, but the deep neural network performed the best when tested using the BoT_IoT dataset while HELAD performed the best when tested using the Stratosphere IoT dataset. So although we found that a deep neural network solution had the highest average F1 scores on tested datasets, it is not always the best-performing one. We further discuss difficulties in using IDS from literature and project repositories, which complicated drawing definitive conclusions regarding IDS selection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice Hesford, Jake Cheng, Daniel Wan, Alan Huynh, Larry Kim, Seungho Kim, Hyoungshick Hong, Jin B. Cryptography and Security Machine Learning 68M25, 68M20 C.4; D.m Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset. For example, BoT_IoT and Stratosphere IoT datasets both capture IoT-related attacks, but the deep neural network performed the best when tested using the BoT_IoT dataset while HELAD performed the best when tested using the Stratosphere IoT dataset. So although we found that a deep neural network solution had the highest average F1 scores on tested datasets, it is not always the best-performing one. We further discuss difficulties in using IDS from literature and project repositories, which complicated drawing definitive conclusions regarding IDS selection. |
| title | Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice |
| topic | Cryptography and Security Machine Learning 68M25, 68M20 C.4; D.m |
| url | https://arxiv.org/abs/2403.17458 |