流量转换器基于流量的网络数据分析的灵活 python 框架

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
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引用次数: 0

摘要

FlowTransformer 是一个软件框架,专为构建基于机器学习的网络入侵检测系统(NIDS)而设计,它利用了在 NLP 和更广泛的数据序列处理方面以高效著称的转换器架构。FlowTransformer 是一个灵活的管道,由可定义的数据集定义、高效的预处理和灵活的模型构建组成,支持不同的输入编码、转换器模型和分类头。此外,用户还可以通过定义自己的组件来扩展该框架。FlowTransformer 的贡献在于它易于定制,并能利用转换器实现增强的长期模式检测,为网络安全研究人员和从业人员提供了一个宝贵的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FlowTransformer: A flexible python framework for flow-based network data analysis

FlowTransformer is a software framework tailored for building Machine Learning based Network Intrusion Detection Systems (NIDSs) leveraging transformer architectures known for their effectiveness in both NLP and more broadly for handling sequences of data. FlowTransformer is a flexible pipeline composed of a definable dataset definition, efficient preprocessing, and a flexible model construction, supporting different input-encodings, transformer models and classification heads. Furthermore, users can extend the framework by defining their own components. FlowTransformer’s contribution lies in its easy customisation, and ability to leverage transformers to enable enhanced long-term pattern detection, offering cybersecurity researchers and practitioners a valuable tool.

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来源期刊
Software Impacts
Software Impacts Software
CiteScore
2.70
自引率
9.50%
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审稿时长
16 days
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