基于两级分段模型的异构数据处理与网络攻击检测

李超鹏; 王劲林

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基于两级分段模型的异构数据处理与网络攻击检测

更新时间：2026-09-04

- 基于两级分段模型的异构数据处理与网络攻击检测
- Heterogeneous data processing and network attack detection based on two-level and multi-segment model
- 微电子学与计算机 2019年36卷第8期页码：54-58
- 作者机构：
  
  1. 中国科学院声学研究所,北京,100190
  2. 中国科学院大学机构,北京,101408
- 作者简介：
- 基金信息：
  
  国家科技重大专项课题 2017ZX03001019
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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李超鹏, 王劲林. 基于两级分段模型的异构数据处理与网络攻击检测[J]. 微电子学与计算机, 2019, 36(8): 54-58.

LI Chao-peng, WANG Jin-lin. Heterogeneous data processing and network attack detection based on two-level and multi-segment model[J]. 2019, 36(8): 54-58.
李超鹏, 王劲林. 基于两级分段模型的异构数据处理与网络攻击检测[J]. 微电子学与计算机, 2019, 36(8): 54-58. DOI：

LI Chao-peng, WANG Jin-lin. Heterogeneous data processing and network attack detection based on two-level and multi-segment model[J]. 2019, 36(8): 54-58. DOI：

摘要

由于网络数据存在着异构特性

在网络攻击检测中

需要使用合理的方法对异构数据进行整合.我们在文章中提出了两级分段模型对异构数据进行整合并建模分析

同时讨论了模型在多核条件下的模型分布式并行训练.在实验部分

本文介绍了使用的DARPA 1998网络攻击数据集

并分析了不同分段方式下的攻击检测性能.同时

本文介绍说明模型在多核条件下的并行性能.结果显示

在面向异构数据时

两级分段模型相比于无分段的全连接神经网络模型有着更好的检测准确率和召回率.

Abstract

Because of the heterogeneous character of network data

a reasonable method is required to integrate the heterogeneous data in network attack detection. In this paper

we propose a two-level and multi-segment model for network attack detection. Meanwhile

the parallel training method is also involved. In the experiments

DARAP 1998 dataset has been used which is a public cyber attacks dataset. The experimental result shows that when facing the heterogeneous data

the two-level and multi-segments model has an effective performance that the proposed model obtains a better detecting accuracy and recall than the fully connected neural network.

关键词

Keywords

references

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