تقرير
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS
| العنوان: | Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS |
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| المؤلفون: | Kajiura, Maho, Nakamura, Junya |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture |
| الوصف: | 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. Comment: This paper was accepted at the 14th IEEE International Workshop on Network Technologies for Security, Administration & Protection (NETSAP 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.13066 |
| رقم الأكسشن: | edsarx.2405.13066 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |