使用贝叶斯核心集分析和存储网络入侵检测数据：离线和流式设置的初步研究
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
摘要：
在本文中，我们提供了一个初步的研究应用贝叶斯核心集的网络安全数据。网络入侵检测是一个可以利用贝叶斯机器学习来建模不确定性和管理流数据的领域;然而，大规模的数据集往往阻碍了基于MCMC的贝叶斯学习方法的使用。限制有用数据的数量是网络流量分析等领域的核心问题，其中通过数据包收集可以非常快速地生成大量冗余数据。减少样本的数量不仅会使学习更可行，而且还有助于减少对记忆和存储的需求。我们在这里探索使用贝叶斯coresets，一种技术，减少数据样本的数量，同时保证学习一个准确的后验分布使用贝叶斯学习。我们分析了贝叶斯核心集如何影响学习模型的准确性，以及在静态场景和流场景中如何权衡时空要求。
英文摘要：
In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bayesian machine learning in modelling uncertainty and managing streaming data; however, the large size of the data sets often hinders the use of Bayesian learning methods based on MCMC. Limiting the amount of useful data is a central problem in a field like network traffic analysis, where large amount of redundant data can be generated very quickly via packet collection. Reducing the number of samples would not only make learning more feasible, but would also contribute to reduce the need for memory and storage. We explore here the use of Bayesian coresets, a technique that reduces the amount of data samples while guaranteeing the learning of an accurate posterior distribution using Bayesian learning. We analyze how Bayesian coresets affect the accuracy of learned models, and how time-space requirements are traded-off, both in a static scenario and in a streaming scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
