Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929353551511552 |
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| author | Kajiura, Maho Nakamura, Junya |
| author_facet | Kajiura, Maho Nakamura, Junya |
| contents | Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in network traffic. In particular, machine-learning-based NIDSs have attracted attention because of their high detection rates of unknown attacks. A distributed processing framework for machine-learning-based NIDSs employing a scalable distributed stream processing system has been proposed in the literature. However, its performance, when machine-learning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative classifiers (Decision Tree, Random Forest, Naive Bayes, SVM, and kNN) based on this framework and evaluate their throughput and latency. By conducting the experimental measurements, we investigate the difference in the processing performance among these classifiers and the bottlenecks in the processing performance of the framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS Kajiura, Maho Nakamura, Junya Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Networking and Internet Architecture Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in network traffic. In particular, machine-learning-based NIDSs have attracted attention because of their high detection rates of unknown attacks. A distributed processing framework for machine-learning-based NIDSs employing a scalable distributed stream processing system has been proposed in the literature. However, its performance, when machine-learning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative classifiers (Decision Tree, Random Forest, Naive Bayes, SVM, and kNN) based on this framework and evaluate their throughput and latency. By conducting the experimental measurements, we investigate the difference in the processing performance among these classifiers and the bottlenecks in the processing performance of the framework. |
| title | Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.13066 |